This paper addresses the problem of state estimation for Markov jump genetic oscillator networks with time-varying delays based on hidden Markov model. Two non-identical types of time-varying delays, that is, the inte...
This paper addresses the problem of state estimation for Markov jump genetic oscillator networks with time-varying delays based on hidden Markov model. Two non-identical types of time-varying delays, that is, the intercellular coupling delay, and the regulatory delay are considered in consideration in genetic oscillator networks. Then a state estimator is designed by solving a set of linear matrix inequalities that can be solved with existing software. Finally, The effectiveness of state estimation approach can then be demonstrated through a numerical example.
Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these m...
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Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.
A high-reliability constant current to constant voltage power supply system has the advantages of small volume of switching power supply, high power density, high efficiency was proposed. This paper use two controller...
A high-reliability constant current to constant voltage power supply system has the advantages of small volume of switching power supply, high power density, high efficiency was proposed. This paper use two controllers to control the shunt regulator(SR) circuit and single-end flyback converter part, and separate the two parts for small signal modeling and give the parameters to stabilize the closed loop. The state space average modeling idea was used to solve the state equations for the modes of the converters in a switching cycle. In order to ensure the stability of cascade system, this paper collaborative optimization of hardware filter parameters and the appropriate PI parameter design. The experimentals verify the correctness of our theory, and the system has good stability under closed-loop conditions.
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 focuses on the bounded tracking control of general linear multi-agent systems(MASs), considering the effects of inevitable communication time-delays, measurement noises, and uncertain disturbances in practi...
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This paper focuses on the bounded tracking control of general linear multi-agent systems(MASs), considering the effects of inevitable communication time-delays, measurement noises, and uncertain disturbances in practical applications. Firstly, the bounded tracking control problem of uncertain MASs under multiplicative noises is transformed into the boundedness problem of stochastic differential delay equations. Then, the upper bound of agent tracking is calculated by means of linear variation, variation of constants formula and stochastic analysis theory, and sufficient conditions are given for the system to achieve the bounded tracking control.
With the rapid development of sequencing technology, researchers can obtain a large number of single cell RNA sequencing (scRNA-seq) data which is useful for analysis of cell fate decision and growth process at indivi...
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The irradiance-power curve is an important basis for examining the operating status of photovoltaic power stations. In the actual operation process, sensor failure, abnormal communication and equipment damage will bri...
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The irradiance-power curve is an important basis for examining the operating status of photovoltaic power stations. In the actual operation process, sensor failure, abnormal communication and equipment damage will bring a large number of abnormal values to the output data of photovoltaic power plants. It will have a significant impact on a variety of applications based on photovoltaic output data. This paper analyzes the typical outliers on the irradiance-power curve and proposes a photovoltaic output data cleaning method based on fuzzy clustering algorithm and quartile algorithm. By comparing with the quartile method, it is proved that this method can effectively identify abnormal data when there are a large number of outliers in the photovoltaic output data.
For hybrid energy storage systems in DC microgrids, a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components, then assign t...
For hybrid energy storage systems in DC microgrids, a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components, then assign them to batteries and supercapacitors to respond respectively. However, aiming at the service life of the energy storage system, this paper considers the characteristics and key parameters of the hybrid energy storage structure and proposes an adaptive drooping comprehensive control strategy considering the SOC of the energy storage unit given the shortcomings of power distribution within the current hybrid energy storage. According to the self-regulation capacity of each energy storage unit, it is sorted and constrained, and protected by using SOC, which ensures the economy and safety of the system while ensuring power distribution. The traditional droop control and adaptive droop control are simulated to verify the effectiveness of the proposed control strategy.
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.
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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