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Active contour model (ACM) is an important branch in the field of image segmentation, since it adapts to different image types and scenes. However, traditional ACMs rely on local image information, which makes them sensitive to initial contours and time-consuming. In this paper, a Laplace operator based ACM (LOACM) with improved image edge detection performance is proposed. LOACM combines the global pre-fitting function which is constructed by using the image's second-order gradient information with the adaptive boundary indicator function to build a hybrid model. Its compact code size allows it to be executed on a wide range of edge devices. To substantiate the segmentation capability of the LOACM model, the BSD500 dataset was utilized for experimentation. The results indicate that LOACM exhibits excellent performance, with average IOU and DSC scores of 0.861 and 0.924 respectively. Additionally, it consistently handles processes within an average time of less than 0.3 s, significantly less than that of comparative models, providing compelling evidence of LOACM's exceptional efficiency and precision. [ABSTRACT FROM AUTHOR]

This paper proposes parameter identification methods for multivariate pseudo-linear autoregressive systems. First, a multivariate generalized stochastic gradient (M-GSG) algorithm is presented as a comparison basis. In order to improve the parameter estimation accuracy, a multivariate multi-innovation generalized stochastic gradient (M-MI-GSG) algorithm and a filtering based multivariate generalized stochastic gradient (F-M-GSG) algorithm are presented by means of the multi-innovation identification theory and the data filtering technique. The simulation results confirm that the proposed algorithms are more effective than the M-GSG algorithm. [ABSTRACT FROM AUTHOR]

What lies beneath our feet? Huge quantities of data and complex analysis have revealed an Earth that is spinning at its core, says Ping Ma. [ABSTRACT FROM AUTHOR]

Chaotic oscillation in a power system is considered the main cause of power blackouts in large-scale interconnected power grids. The chaotic oscillation mechanisms and the control methods for chaos oscillation of power systems need to be analyzed. This paper thus proposed an adaptive control method for chaotic power systems using finite-time stability theory and passivity-based control approach. The adaptive feedback controller is first constructed using the finite-time stability theory and the passive theory to make the chaotic power system equivalent to a closed-loop passive system. We then proved that the passive power system can stabilize the equilibrium points. We also extensively studied fourth-order power system. Results show that the controller based on the finite-time theory and the passivity-based control approach can effectively stabilize the chaotic behavior within finite time. The control strategy was also found to be robust to the different power system states. [ABSTRACT FROM AUTHOR]

• The research on the complex bursting behaviors in the permanent magnet synchronous motor system with external load perturbation has barely been reported, so this paper shows that the system has bursting oscillations, which enrich the dynamic behaviors in the motor system. • Permanent magnet synchronous motor system with external load perturbation can produce fast-slow coupling effect that is the prerequisite for generating bursting oscillations. • Changes in the system's own parameters and changes in the amplitude of external load perturbation can cause permanent magnet synchronous motor system to exhibit different types of bursting oscillations. • This paper reveals the bursting mechanisms through using fast–slow dynamics analysis method in detail and summarizes the ranges of parameters corresponding to different operating states of the motor. This research aims to study the bursting oscillations and bifurcation mechanism in a permanent magnet synchronous motor (PMSM) system with external load perturbation. When an order gap exists between the external load perturbation frequency and the natural frequency of the PMSM system, the PMSM system exhibits bursting oscillation behaviors. First, the mathematical model of a PMSM with external load perturbation is established by considering the external load perturbation as a slow-varying parameter. The PMSM system is transformed into a generalized autonomous PMSM system. Second, the Fold and Hopf bifurcation conditions of the PMSM system are obtained through bifurcation analysis. Third, the bursting oscillations and bifurcation mechanism of the PMSM system are analyzed in detail by using equilibrium point distribution and bifurcation diagrams, transformed phase portraits, and time series diagrams. The PMSM system exhibits periodic symmetric Fold/Fold bursting oscillations and periodic delayed subHopf/subHopf bursting oscillations, as the parameters change. When the amplitude of the external load perturbation changes and the PMSM system parameters are fixed, the bursting oscillation processes in different ranges of amplitude are revealed. [ABSTRACT FROM AUTHOR]

• A finite-time function projective synchronization control method with parameter observers for chaotic wind power systems is proposed. • A high-dimensional wind power system is built and its chaotic behaviors are analyzed. • Chaos control of wind power systems with or without uncertain parameters are considered. • The mathematical proof of the stability theorem is given, and the corresponding numerical simulation is provided. Wind power is a rapidly growing renewable energy source that plays an increasingly important role in power systems. However, its dynamic behaviors are complex because of the instability of wind speed, strong coupling, highly nonlinear subsystems, and mass grid connections. Chaotic oscillation is one of the most typical complex dynamic behaviors of wind power systems. This behavior can seriously influence the stable operation of wind power systems and cause great harm. This study proposed a control method for finite-time function projective synchronization on the basis of the chaos synchronization principle and finite-time theory for wind power systems. Wind power systems with or without uncertain parameters were considered. First, we built a high-dimensional wind power system and analyzed its chaotic behaviors. Lyapunov exponents were derived to prove the existence of chaos and bifurcation. Second, we aimed to increase the robustness of the controller by adding parameter observers to the controller. The result helped solve the problem of unknown parameters. If a wind power system comprises unknown parameters, the unknown parameters can be defined as the system's states. An unknown parameter observer was designed to realize the identification of unknown parameters. Third, we proposed a control method for finite-time function projective synchronization. Finite-time stability theory and function projective synchronization theory were used to construct the controller. These theories can ensure the quick synchronization of a chaotic wind power system with a stable wind power system in finite time. Finally, the mathematical proof of the stability theorem was derived, and a corresponding numerical simulation was performed to validate the chaos control method for wind power systems. [ABSTRACT FROM AUTHOR]