At present, most research on the coverage of multi-agent systems is based on Euclidean distance. This does not consider the existence of obstacles and has great limitations in the application. In this paper, a kind of...
At present, most research on the coverage of multi-agent systems is based on Euclidean distance. This does not consider the existence of obstacles and has great limitations in the application. In this paper, a kind of coverage control problem based on high-order geodesic Voronoi partition is practically investigated. It allows multiple agents to monitor an area with obstacles to achieve the monitoring of the overall environment. As a result, the geodesic distance is introduced as a metric form. Based on the geodesic distance, point-by-point scanning on the layer is taken to achieve high-order Voronoi diagram division. The coverage algorithm can be implemented in a distributed manner through the exchange of location information with each other, and the Lloyd algorithm is added to realize the movement of the sensor toward the optimal position.
In this paper, the events-based model predictive control (MPC) problem is studied for systems under false data injection (FDI) attacks. A time-varying event-triggered mechanism (ETM) is proposed to manage measurement ...
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In this paper, the events-based model predictive control (MPC) problem is studied for systems under false data injection (FDI) attacks. A time-varying event-triggered mechanism (ETM) is proposed to manage measurement data packet releases and a static ETM is used to reduce the influence of the FDI attacks on the controller. By using the properties of the defined robust positive invariant set, a solvable auxiliary optimization problem (OP) is proposed to design the controller. The recursive feasibility of the auxiliary OP and the input-to-state stability of the closed-loop system are guaranteed. The validity of the developed ETMs-based anti-attack MPC algorithm is shown by an example.
Landslide displacement prediction is an important and indispensable part of landslide monitoring and warning. The change of the displacement is always considered being related to inducing factors, which are aimed at i...
Landslide displacement prediction is an important and indispensable part of landslide monitoring and warning. The change of the displacement is always considered being related to inducing factors, which are aimed at improving accuracy of the predicted model. However, the seasonal characteristic of the displacement, which has not been carefully analyzed, reveals the law of inducing factors. In order to gain a deeper understanding of characteristics, the Baijiabao landslide is taken as an example. The variational mode decomposition (VMD) method, which can extract effective information well, is introduced to decompose the displacement. Introducing the seasonal parameters, the seasonal autoregressive integrated moving average (SARIMA) model is established to predict the displacement subseries. Finally, accumulative displacement prediction values are obtained by superimposing the predicted subseries. With higher accuracy and lower error, the VMD-SARIMA model proves a better option in application compared with VMD-ARIMA, SARIMA and ARIMA models.
Molten iron is the primary output of blast furnace production. The content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace product...
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Molten iron is the primary output of blast furnace production. The content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace production, the silicon content in molten iron is nonlinear and unstable. Therefore, this paper adopts variational mode decomposition (VMD) to decompose and extract the feature information of the real silicon content data of LY Steel in March 2022, then uses Grey Wolf optimization (GWO) algorithm to optimize the parameters of the support vector regression (SVR) prediction model, and takes the decomposed data as model input for experimental verification. By comparing the predicted results with the real historical data of blast furnace production, it is found that the degree of fit is about 94.2%, which offers a new idea for the prediction of silicon content.
This paper addresses the robust finite-time stabi-lization (FTS) issue for stochastic parabolic PDE systems via non-fragile spatial sampled-data control scheme. First, a class of distributed parameter systems characte...
This paper addresses the robust finite-time stabi-lization (FTS) issue for stochastic parabolic PDE systems via non-fragile spatial sampled-data control scheme. First, a class of distributed parameter systems characterized by the delayed stochastic parabolic partial differential equation is developed for analyzing the effects of stochastic disturbance, structural uncertainty, and discrete delay on the system performance. Then, a non-fragile spatial sampled-data control scheme is established by setting sampling points in the spatial domain, which effectively saves communication resources and ensures that the closed-loop system maintains good performance when the controller is perturbed. Moreover, based on the partial differential equation theory, stochastic analysis approach, and the extended Wirtinger's inequality technique, several criteria are provided to ensure the robust FTS of stochastic parabolic PDE systems in the mean square sense. Lastly, a numerical example is provided to verify the feasibility of the suggested stabilization criteria and control scheme.
Landslide disasters are extremely destructive. Accurate identification of landslides plays an important role in disaster assessment, loss control and post-disaster reconstruction. This paper proposes a semantic segmen...
Landslide disasters are extremely destructive. Accurate identification of landslides plays an important role in disaster assessment, loss control and post-disaster reconstruction. This paper proposes a semantic segmentation landslide identification method based on improved U-Net. The deep convolution neural network and jump connection method is used for end-to-end semantic segmentation to achieve deep feature extraction and fusion of different receptive fields, thus enriching feature information. SENet modules are adopted to enhance the ability of the model to extract important features, so as to further improve the accuracy of model recognition. Extensive experiments show that our improved U-Net achieves better performance than the original algorithm on our landslide datasets. The results of Iou are improved by 4.12% which demonstrates our work is of great significance for the research of landslide area identification. Finally, the model is deployed to the web and applied to the geological hazard intelligent monitoring system to realize the landslide identification task.
Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing.A dynamical modeling method for ...
Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing.A dynamical modeling method for the offshore drilling experimental system built on the double-layer Stewart parallel mechanism is ***,the kinematic and dynamical characteristics of the double-layer Stewart parallel mechanism are combined with the Lagrange method and the virtual work method to establish the dynamics model of the *** a parameter identification scheme is designed using a nonlinear gray system estimation method based on the trust-domain reflection algorithm,and the model parameters are *** model is downscaled to improve the feasibility of the identification scheme and the accuracy of the identified *** actual experimental system data verify this model's correctness and the model parameters' accuracy.
The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or s...
The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or simple texture. However, many tiles with complex and irregular surface patterns are used in practical applications, but many methods cannot effectively detect surface defects in such tiles. This paper presents a double input feature difference network structure to overcome the limitation. First, a double input channel is constructed to extract features from the template image and the defect image respectively. Next, a method of feature difference is performed at different depths to suppress the background interference and prevent misclassification between different defect categories. Then a parameter-free attention module is embedded in the backbone to improve the ability of feature extraction. Experimental results show that this model effectively improves the mean average accuracy of 8.3% and the recall rate of 11.7%.
Single cell RNA sequencing (scRNA-seq) technology can study gene expression in single cell resolution and solve cell heterogeneity that cannot be solved by the traditional RNA sequencing (Bulk RNA-seq) technology. It ...
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A fault diagnosis method based on Discrete Hidden Markov Models is proposed in this paper to identify the fault causing alarm flood sequences. The proposed method consists of the following steps: First, the alarm floo...
A fault diagnosis method based on Discrete Hidden Markov Models is proposed in this paper to identify the fault causing alarm flood sequences. The proposed method consists of the following steps: First, the alarm flood data is pre-processed to ensure that all sequences are of uniform length, and a separate Discrete Hidden Markov model is trained for each fault to capture the relationship between the fault and the alarm sequences. Second, given an observation sequence, the log-likelihood probability values under different Discrete Hidden Markov models are calculated and the maximum probability is selected to determine the type of corresponding fault. Last, the feasibility of the proposed method is verified by simulation data obtained from a public industrial model. The results show that the method can effectively identify the faults that trigger alarm floods.
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