Search

This paper analyzes the English teaching model’s structure, function, and characteristics and constructs the basic structure of the classroom innovation teaching model. Then, the English teaching model is constructed, and the English teaching evaluation index system is established based on the algorithms of structural equation model, multigroup structural equation and multigroup parameter estimation. Finally, the quantitative analysis of index weights and satisfaction was conducted with the example of college A. The results showed that the weights of the primary indicators were 0.2873, 0.1294, 0.2546, and 0.3287 for personalized needs, perceived quality, perceived value, and satisfaction, respectively, which indicated that the multigroup structural model could help universities to realize the innovation of English teaching mode and to adopt diversified teaching methods to meet student’s diverse needs in the teaching process. Diversified the needs of students and improved their satisfaction.

Object detection in underwater environments presents significant challenges due to the inherent limitations of sonar imaging, such as noise, low resolution, lack of texture, and color information. This paper introduces AquaYOLO, an enhanced YOLOv8 version specifically designed to improve object detection accuracy in underwater sonar images. AquaYOLO replaces traditional convolutional layers with a residual block in the backbone network to enhance feature extraction. In addition, we introduce Dynamic Selection Aggregation Module (DSAM) and Context-Aware Feature Selection (CAFS) in the neck network. These modifications allow AquaYOLO to capture intricate details better and reduce feature redundancy, leading to improved performance in underwater object detection tasks. The model is evaluated on two standard underwater sonar datasets, UATD and Marine Debris, demonstrating superior accuracy and robustness compared to baseline models. [ABSTRACT FROM AUTHOR]

Summary: The event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments. [ABSTRACT FROM AUTHOR]

This paper designs a resilient stubborn observer for switched autonomous systems (SASs) with an energy harvesting sensor (EHS). In order to extend the lifespan of the sensor, a sensor with the ability of harvesting energy is used. For restraining the effect of measurement outliers, a stubborn observer is designed which includes a saturation function with the varying saturation levels. The observer gain fluctuation is considered which is dealt with by the resilience filtering scheme. The aim of this paper is, considering observer gain fluctuation, the measurement outliers and an EHS for SASs, to design a resilient stubborn observer which can guarantee the error dynamics to meet the given performance requirement. Specifically, the existence conditions are derived firstly and then the resilient stubborn observer is designed by linear matrix inequality (LMI) technology. At the end, the performance of the designed observer is evaluated through a numerical simulation and a set of comparative experiments are implemented to further signify the merits of the designed observer. [ABSTRACT FROM AUTHOR]

In this paper, the localization problem of the mobile robot (MR) with an energy harvesting sensor (EHS) is studied. A specific situation is regarded, which is that the MR equips with an EHS who owns the capacity for absorbing the energy from the surroundings. When the sensor energy is sufficient, the sensor can send its measurement to the filter. Otherwise, the sensor energy‐induced measurement missing phenomenon will occur. For the sake of acquiring the measurement missing rate, the relationship between the amount of the sensor energy and its probability distribution is derived recursively at each time instant. The purpose of this paper is to seek out a feasible solution to the addressed MR localization (MRL) problem (MRLP) by devising the desired time‐varying filter. Firstly, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) for the filtering error (FE) covariance (FEC) is acquired recursively. Then, the acquired UB is minimized by characterizing the desired filter gain adequately. Finally, a simulation is conducted to testify the availability of the proposed MRL algorithm (MRLA). [ABSTRACT FROM AUTHOR]

This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy‐induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the mobile robot localization problem subject to filter gain uncertainty under dynamic event‐triggered communication mechanism, and meanwhile, the H∞ filtering performance and the error variance constraint are guaranteed. For saving the sensor energy, a dynamic event‐triggered communication mechanism is introduced to manage the transmission of the measurement data from the sensor to the filter. To characterize the possible fluctuations of the desired filter gain, a resilient filter is constructed for the mobile robot localization. The aim of this paper is to find a solution to the mobile robot localization problem by designing a nonlinear resilient filter such that the filtering error dynamics satisfies both the H∞ performance requirement and the error variance constraint over a finite time horizon simultaneously. By resorting to the Lyapunov theory and the stochastic analysis technique, the sufficient conditions are established to guarantee that the error dynamic system satisfies both the H∞ performance requirement and the error variance constraint. Then, a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter. Based on the proposed filter design scheme, the corresponding localization algorithm is presented. Finally, an experiment is conducted in the simulation environment to verify the effectiveness of the proposed localization algorithm. [ABSTRACT FROM AUTHOR]