Global dynamics for a Filippov system with media effects

<abstract><p>In the process of spreading infectious diseases, the media accelerates the dissemination of information, and people have a deeper understanding of the disease, which will significantly change their behavior and reduce the disease transmission; it is very beneficial for people to prevent and control diseases effectively. We propose a Filippov epidemic model with nonlinear incidence to describe media's influence in the epidemic transmission process. Our proposed model extends existing models by introducing a threshold strategy to describe the effects of media coverage once the number of infected individuals exceeds a threshold. Meanwhile, we perform the stability of the equilibriua, boundary equilibrium bifurcation, and global dynamics. The system shows complex dynamical behaviors and eventually stabilizes at the equilibrium points of the subsystem or pseudo equilibrium. In addition, numerical simulation results show that choosing appropriate thresholds and control intensity can stop infectious disease outbreaks, and media coverage can reduce the burden of disease outbreaks and shorten the duration of disease eruptions.</p></abstract>

Accurate Instance Segmentation for Remote Sensing Images via Adaptive and Dynamic Feature Learning

Instance segmentation for high-resolution remote sensing images (HRSIs) is a fundamental yet challenging task in earth observation, which aims at achieving instance-level location and pixel-level classification for instances of interest on the earth’s surface. The main difficulties come from the huge scale variation, arbitrary instance shapes, and numerous densely packed small objects in HRSIs. In this paper, we design an end-to-end multi-category instance segmentation network for HRSIs, where three new modules based on adaptive and dynamic feature learning are proposed to address the above issues. The cross-scale adaptive fusion (CSAF) module introduces a novel multi-scale feature fusion mechanism to enhance the capability of the model to detect and segment objects with noticeable size variation. To predict precise masks for the complex boundaries of remote sensing instances, we embed a context attention upsampling (CAU) kernel instead of deconvolution in the segmentation branch to aggregate contextual information for refined upsampling. Furthermore, we extend the general fixed positive and negative sample judgment threshold strategy into a dynamic sample selection (DSS) module to select more suitable positive and negative samples flexibly for densely packed instances. These three modules enable a better feature learning of the instance segmentation network. Extensive experiments are conducted on the iSAID and NWU VHR-10 instance segmentation datasets to validate the proposed method. Attributing to the three proposed modules, we have achieved 1.9% and 2.9% segmentation performance improvements on these two datasets compared with the baseline method and achieved the state-of-the-art performance.

On optimal thresholds for pairs trading in a one-dimensional diffusion model

We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed. doi:10.1017/S1446181121000298

Algorithms for Solving Inverse Problems of Simulation Modeling

This paper is devoted to solving inverse problems of simulation modeling, which are presented in the form of an optimization problem. The article discusses the use of direct search methods taking into account the specifics of the problem under consideration. Due to the fact that these methods require a lot of computational experiments, two algorithms based on approximation were proposed for solving the problem. The first algorithm consists in determining and evaluating the parameters of the function of dependence (which can be linear or non-linear) of the output variable on the input variables and solving the inverse problem by minimizing increments of the arguments. In the second algorithm a linear function of dependence is iteratively constructed using the data set generated by changing the input variables in given increments, and the inverse problem is solved by minimizing increments of the arguments. The classical inventory management model with a threshold strategy is considered as an example. The inverse problem was solved using direct search and approximation-based methods.

ON OPTIMAL THRESHOLDS FOR PAIRS TRADING IN A ONE-DIMENSIONAL DIFFUSION MODEL

We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed.

Adaptive and Robust Operation with Active Fuzzy Harvester under Nonstationary and Random Disturbance Conditions

The objective of this paper is to amplify the output voltage magnitude from a piezoelectric vibration energy harvester under nonstationary and broadband vibration conditions. Improving the transferred energy, which is converted from mechanical energy to electrical energy through a piezoelectric transducer, achieved a high output voltage and effective harvesting. A threshold-based switching strategy is used to improve the total transferred energy with consideration of the signs and amplitudes of the electromechanical conditions of the harvester. A time-invariant threshold cannot accomplish effective harvesting under nonstationary vibration conditions because the assessment criterion for desirable control changes in accordance with the disturbance scale. To solve this problem, we developed a switching strategy for the active harvester, namely, adaptive switching considering vibration suppression-threshold strategy. The strategy adopts a tuning algorithm for the time-varying threshold and implements appropriate intermittent switching without pre-tuning by means of the fuzzy control theory. We evaluated the proposed strategy under three realistic vibration conditions: a frequency sweep, a change in the number of dominant frequencies, and wideband frequency vibration. Experimental comparisons were conducted with existing strategies, which consider only the signs of the harvester electromechanical conditions. The results confirm that the presented strategy achieves a greater output voltage than the existing strategies under all nonstationary vibration conditions. The average amplification rate of output voltage for the proposed strategy is 203% compared with the output voltage by noncontrolled harvesting.

Global dynamics for a non-smooth Filippov system with the threshold strategy

In this study, the Leslie-Gower model with functional response is extended into a non-smooth Filippov system by applying IPM strategies. Once the number of pests reaches or surpasses the given economic threshold(ET), spraying pesticides and releasing the natural enemy are implemented simultaneously. In order to maintain the pest population at or below ET, global dynamics of the proposed model are investigated completely, including the existence of sliding mode and various equilibria, sliding dynamics and global stability of equilibria. The result shows that real equilibrium cannot coexist with the unique pseudo-equilibrium. In particular, after excluding the existence of any possible limit cycle, the global stability of equilibria is obtained by employing qualitative and numerical techniques. In the end, the effect of our work on pest control are discussed.

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.