Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Monte Carlo integration is a numerical integration method using random numbers. The speed of convergence of the Monte Carlo integration can be faster by using appropriate chaotic random numbers generated by one-dimensional chaotic maps. This paper discusses the efficiency of Monte Carlo integration using chaotic random numbers generated by tent maps with a uniform distribution.
Critical Infrastructures such as transportation or energy systems are central to the existence of modern societies. Due to the expected level of performance and developments like the shift towards renewable, fluctuating energy generation in Smart Grids, advanced monitoring and control systems are required for stable operation. This in turn increases the dependence on robust communication technology...
Aggregation protocols allow for distributed lightweight computations deployed on ad-hoc networks in a peer-to-peer fashion. Due to reliance on wireless technology, the communication medium is often hostile which makes such protocols susceptible to correctness and performance issues. In this paper, we study the behavior of aggregation protocols when subject to communication failures: message loss,...
This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two coefficients, namely the exploration rate and the exploitation rate. These coefficients are updated in each run of the algorithm and provide an appropriate balance between the exploration...
With the intelligentization of distribution network, a large scale of power electronic devices are applied in energy conversion. And the system flow of distribution network becomes random and varies greatly, which puts forward higher requirements for measurement and protection. Therefore, it's necessary to detect and identify short-circuit fault rapidly and effectively. In this paper, an early detecting...
In this paper, the convergence and the stability analysis of two Induction Motor (IM) rotor time constant (Tr) estimators are performed. Both estimators are Model Reference Adaptive System (MRAS) based, first considering the rotor flux vector, second the reactive power as the state variable. The stability analysis is based on Lyapunov and Popov criteria, passivity formalism and positive real function...
This paper presents an Unscented Kalman filter (UKF) based algorithm for estimating available bandwidth in a space Disruption-Tolerant Network (DTN) link. In the proposed algorithm, an UKF model is proposed for accurately tracking available bandwidth of bundle delivery over a space link. In particular, the proposed UKF will iteratively filter a serial of noised measurements on successive time intervals,...
This paper has as a start point the metaheuristic Particle Swarm Optimization (PSO), which has very good abilities to solve many types of optimization problems. As a main contribution, this work proposes an intelligent algorithm derived from PSO. This algorithm has two main characteristics. The first one consists in the use of an improved version of PSO, namely Hybrid Topology Particle Swarm Optimization...
This paper presents a stabilizing model predictive control (MPC) algorithm based on the off-line computation of sequence of 1-step controllable sets for linear parameter varying systems and a parameter learning technique that improves its performance. The presented MPC algorithm guarantees non-monotone convergence towards a suitably chosen terminal set regardless of the system parameters, while the...
The traditional iterative closest point (ICP) algorithm could register two points sets well, but it is easily affected by local dissimilar. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First, an objective function is proposed under the guidance of the corner points, as the corner points can preserve the similar of the whole shapes...
Based on the variable step-size afïîne projection algorithm, the idea of multiple forgetting factors and variable projection order is proposed. On the one hand, the accurate estimation of error energy is realized in order to achieve faster convergence rate and lower final misalignment. On the other hand, the reduction of computational complexity can be achieved. The simulation results show that the...
In this paper, we investigate distributed consensus problems for multiple miniature aerial vehicles (MAVs) with nonlinear dynamics and uncertainty. We develop distributed consensus protocol to solve regulation synchronization problem for leaderless MAVs with directed interaction topology. Adaptive control algorithms are used locally for each vehicle to deal with nonlinear dynamics and uncertainty...
For two-qubit systems with a degenerate measurement operator, this paper presents a control approach to achieve rapid stabilization to a target Bell state. This approach uses two control channels where the control laws associated with these two channels are designed as a constant and a switching control law, respectively. In the control scheme, the system state space is divided into two parts: the...
Coordination problems including miscoordination and relative overgeneralization are difficult to overcome especially in dynamic and stochastic environments. In the practical scenario, there may be a large number of agents, and the interactions between agents may be sparse and unfixed. In this paper, we study the coordination problems and stochastic rewards under the social learning framework where...
Predicting the desirability and number of student applications to universities is a challenging and dynamic undertaking. Student applications are affected by a variety of factors ranging from those related to the university itself and its surrounding community, to factors up to the national level, including perceptions of international relations. This paper proposes a way of modeling this complex...
This study rewrote a fractional-order particle swarm optimizer algorithmic equation and used an improved uniform design method (IUDM) to find the best combination for parameters of FPSO. Compared to PSO, FPSO makes a high convergence rate. In the improved FPSO, there are 4 parameters to influence effectiveness. Uniform design is an experimental method and suitable for multiple parameters and multiple...
It is known that the Gradient descent bit flipping (GDBF) algorithm is an effective hard-decision decoding algorithm for low-density parity-check (LDPC) codes. However, trapping in a local maximum limits its error-rate performance. This paper presents a modified GDBF scheme that can mitigate the trapping problem and hence can improve the error-rate performance. Compared to the conventional GDBF algorithm,...
In massive multiple-input multiple-output (MIMO) mobile system, the computational complexity of signal detection increases exponentially along with the growing number of antennas. For example, the sub-optimal linear detection schemes, such as zero forcing (ZF) detector and minimum mean square error (MMSE) detector, always have to balance the performance and complexity resulted from the large-scale...
This paper presents a real-time implementation of an identification-based linear control law on a twin rotor MIMO system. Identification is performed via dynamical neural networks which-under mild conditions-guarantee exponential convergence to zero of the identification error. Once the plant model is obtained and linearized, a stabilizing linear state feedback control law is synthesized via linear...
Due to the rapid growth in scale and complexity of information networks, self-organizing systems have been focused on for realizing new network control architectures that have high scalability, adaptability, and robustness. However, in self-organizing systems, the uncertainty (incompleteness, ambiguity, and dynamicity) of information observable for components in the system can lead to the slow adaptation...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.