This paper studies the stability of the six-dimensional Fractional gene regulatory networks(GRNs) with three variables and the bifurcation of second-order system *** paper presents the basic interaction model figure o...
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This paper studies the stability of the six-dimensional Fractional gene regulatory networks(GRNs) with three variables and the bifurcation of second-order system *** paper presents the basic interaction model figure of three variables GRNs,which contains the relationship between the three ***, in order to improve the accuracy of the study, in analysis of ***, a numerical simulation example is provided to verify the effectiveness and the advantage of the proposed stability and bifurcation criterion.
The concentration detection of the mixed gas is significant to operate safely and efficiently as well as reduce the emission of pollutants. In this paper, a concentration detection system based on acoustic relaxation ...
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Distance distributions are a key building block in stochastic geometry modelling of wireless networks and in many other fields in mathematics and science. In this paper, we propose a novel framework for analytically c...
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Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing desc...
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We present comparator-based gene network designs for feedback control of the concentration of gene products. Two gene circuit designs are proposed, each of which compares concentrations of an input transcription facto...
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It is well known that one of the key technologies in train control system is the localization of train. Since multiple sensors can be installed on the train to obtain real-time data, information fusion is a promising ...
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ISBN:
(数字)9781728176871
ISBN:
(纸本)9781728176888
It is well known that one of the key technologies in train control system is the localization of train. Since multiple sensors can be installed on the train to obtain real-time data, information fusion is a promising method that can be used to combine each sensor's unique information to calculate the stable and accurate localization results. A multi-sensor based localization system for the train autonomous control is proposed in this paper, which contains the Global Positioning System, Inertial Navigation System and velocity sensor. Covariance Intersection algorithm is proposed to integrate the output results of each sensor. Besides, considering the variety of the train running environment, adaptive Kalman filter is applied to reduce the impact of environmental noise. Finally, simulation results prove the proposed method in this paper improves the positioning accuracy compared with the traditional methods.
As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies...
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This paper presents an economic model predictive control(EMPC) scheme to manage the grid-connected micro-grid in an economic way. The grid-connected micro-grid system is composed of wind generation unit, photovoltaic ...
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This paper presents an economic model predictive control(EMPC) scheme to manage the grid-connected micro-grid in an economic way. The grid-connected micro-grid system is composed of wind generation unit, photovoltaic generation unit,energy storage battery, micro-turbines and load. The generation units are scheduled optimally to achieve the three objectives of power balance, economic performance and environmental protection. Moreover, the analytic hierarchy process(AHP) is used to determine the weight coefficients of the objective function. Simulation and analysis results for the economic optimization of micro-grid system are given to illustrate the effectiveness of the proposed economic model predictive control framework.
The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with "human-designed" methods, which began wi...
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—Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserv...
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