Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities were primarily utilizing newspaper and later television to inform the public, with the rise of the Internet and online social media, the political arena has transformed into a more complex structure. Now, more than ever, people express themselves online while mainstream news agencies attempt to seize the power of the Internet to spread their agenda. To grasp the political coexistence of mainstream media and online social media, in this paper, we perform an analysis between these two sources of information in the context of the U.S. 2020 presidential election. In particular, we collect data during the 2020 Democratic Party presidential primaries pertaining to the candidates and by analyzing this data, we highlight similarities and differences between these two main types of sources, detect the potential impact they have on each other, and understand how this impact relationship can change over time. To supplement these two main sources and to establish a baseline, we also include Google Trends search results and Polling results for each of the candidates that are being analyzed.
No abstract is provided for this article.
The paper proposes an adaptive controller design for Markov jump systems with mixed mode transition information through a reduced-order sliding mode approach. The stability criteria and mode-dependent adaptive control law are achieved using linear matrix inequality technique. Firstly, a linear reduced-order sliding surface function is proposed to achieve the reduced-order sliding mode dynamics. Secondly, a feasible approach is presented to check the stochastic stability of resulting sliding motion corresponding to different mode transition information, and to solve the controller gains from stability criteria. Thirdly, an adaptive sliding mode controller is also designed to ensure the finite-time reachability of the predefined hyperplane even when no mode information is available. Finally, the application of the proposed control strategy to the RLC circuit is provided.
The problem of finite time stability and synthesis is discussed for nonlinear discrete time-varying systems by using the T-S fuzzy model. To reduce the communication network resources, two adaptive event-triggered mechanisms (AETMs) are taken into consideration in sensor-to-observer (S/O) and observer-to-controller (O/C) channels. The thresholds of the AETMs are adjusted according to the estimation error rather than some fixed ones. Sufficient conditions for developing an observer-based time-varying control are obtained, which ensure the time-varying error system to be finite time stable and satisfy the H ∞ performance simultaneously. Moreover, using the singular value decomposition technique, recursive linear matrix inequalities conditions are obtained for computing the gains of the controller and observer. Finally, the superiority and applicability of the proposed method are demonstrated by examples.
This report considers attempts to develop dummy motorcyclists with breakable legs. Material characteristics are discussed. The variation in the scatter fracture load of different materials is compared using the Weibull modulus. The materials used in the different dummy legs have been calibrated statically and uni-axially whereas in crash tests multi-axial dynamic loads are sustained. The Independent Action criterion is used to show that: (1) compressive and torsion loads have only a small effect on bending; and (2) differences in results from different laboratories is the result of scatter in the material characteristics. The effect that leg fracture has on dummy trajectory is described using previously published experimental pedestrian impacts, motorcycle crash tests and pedestrian and car occupant computer simulation studies. Head trajectory is shown to be largely unaffected by leg fracture. For the covering abstract of the conference see IRRD 864606.
This paper is concerned with the problem of dynamic output feedback (DOF) control for a class of uncertain discrete impulsive switched systems with state delays and missing measurements. The missing measurements are modeled as a binary switch sequence specified by a conditional probability distribution. The problem addressed is to design an output feedback controller such that for all admissible uncertainties, the closed-loop system is exponentially stable in mean square sense. By using the average dwell time approach and the piecewise Lyapunov function technique, some sufficient conditions for the existence of a desired DOF controller are derived, then an explicit expression of the desired controller is given. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
In this paper, we present a novel Iterative Linear Matrix Inequality (ILMI) strategy for controller design that makes it possible to compute suboptimal H ∞ static output-feedback (SOF) controllers with high-performance characteristics.The obtained SOF controllers can be effective in reducing the vibrational response of multi-degree-of-freedom structures subjected to broad-band excitations.To demonstrate the effectiveness of the proposed methodology, a SOF controller is designed for the seismic protection of a multi-actuated five-story shear-frame structure with positive results.
This paper deals with H ∞ static output-feedback control design with constrained information for offshore wind turbines. Constrained information indicates that a special zero–nonzero structure is imposed on the static output-feedback gain matrix. A practical use of such an approach is to design a decentralized controller for a wind turbine. This will also benefit the controller in such a way that it is more tolerant to sensor failure. Furthermore, the model under consideration is obtained by using the wind turbine simulation software FAST. Sufficient conditions to design an H ∞ controller are given in terms of Linear Matrix Inequalities ( LMI s ). Simulation results are given to illustrate the effectiveness of the proposed methodology for different cases of the control gain structures.
Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the "dimension disaster" once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.
This paper is concerned with the problems of stability and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -gain analysis for a class of switched positive systems with time-varying delays and actuator saturation. Firstly, a convex hull representation is used to describe the saturation behavior. By constructing a multiple co-positive Lyapunov functional, sufficient conditions are provided for the closed-loop system to be locally asymptotically stable at the origin of the state space under arbitrary switching. Then, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -gain performance analysis in the presence of actuator saturation is developed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.
As the requirements on powertrain efficiency of electric vehicles (EVs) are increasing, integrated motor-transmission (IMT) powertrain systems for EVs are becoming a promising solution. For the integration of IMT powertrain systems, the system state information and the actuator status are usually required for the closed-loop controller design or the on-board fault diagnosis. Embracing the demands, an observer for simultaneous estimation of input and system state of an IMT powertrain system is studied in this paper. It is well-known that controller area network (CAN) has been dominant in the vehicle network, which is used to communicate among controllers, sensors, and actuators. However, the CAN bus always induces time-varying delays when there are a number of communication nodes on the bus. The CAN-bus induced delay would result in vibrations in the vehicle powertrain or even deterioration of the entire closed-loop system. To deal with the CAN-bus induced delay in the estimation work for IMT powertrain systems, the potential random delays are considered in a three-state nonlinear model which represents the behavior of an IMT system. To estimate the input and state simultaneously, an adaptive unscented Kalman filter (AUKF) is adopted. As we know, the adopted AUKF has the benefits of dealing with system nonlinearities and calculating the noise covariance matrix automatically. Simulations and comparisons are carried out. We can see from the results that the proposed observer estimates the input and system state well. Moreover, the resulting estimation error is smaller comparing with the estimation error of the observer based on extended Kalman filter algorithm.
This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs). Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization) algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
No abstract is provided for this article.
