The time-delay delay phenomenon is a kind of widespread physical and biological phenomenon. The existence of time-delay not only give the stability of system analysis and controller design brings great difficulties bu...
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ISBN:
(纸本)9783037859537
The time-delay delay phenomenon is a kind of widespread physical and biological phenomenon. The existence of time-delay not only give the stability of system analysis and controller design brings great difficulties but also usually make the systems unstable and even cause the system performance deteriorated. We use the adaptive dynamic iterative algorithm to solve this equation. By using the neural network to achieve the iterative algorithm, get the optimal control law of the systems with time delay. The simulation results show that the adaptive dynamic programming method to solve the optimal control of the nonlinear system is effective.
The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for *** propose an iterative method for obtaining more precise ...
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The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for *** propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single ***,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure *** technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme *** has significant implications for studying the equation of state and phase transitions.
An adaptive model is proposed to describe time-varying seasonal effects. The seasonal average function is constructed using an iterative algorithm that provides a neat decomposition of the signal into a generalized tr...
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An adaptive model is proposed to describe time-varying seasonal effects. The seasonal average function is constructed using an iterative algorithm that provides a neat decomposition of the signal into a generalized trend, seasonal and residual components. By a trend, we mean long-term evolutionary changes in the average signal level, both unidirectional and chaotic, in the form of a slow random drift. This algorithm allows one to obtain unbiased estimates for each of the signal components, even in the presence of a significant number of missing observations. The series length is not required to be a multiple of an integer number of years. In contrast to the usual "Climate Normals" (CN) model, the considered adaptive model of seasonal effects assumes a continuous slow change in the properties of the seasonal component over time. The degree of allowable variability in seasonal effects from year to year is entered as a tunable parameter of the model. In particular, this allows one to show the dynamics of the growth of the amplitude of seasonal fluctuations in time in the form of a continuous (smooth) function without necessarily linking these changes to predetermined calendar epochs. The algorithm was tested on the atmospheric CO2 concentration monitoring series at Barrow, Mauna Loa, Tutuila, and South Pole stations located at different latitudes. The form of the seasonal variation was estimated, and the average amplitude of the seasonal variation and the rate of its change at each station were calculated. Noticeable differences in the dynamics of the studied parameters between stations are demonstrated. Mean amplitude of seasonal variation in CO2 concentration at Barrow, Mauna Loa, Tutuila, and South Pole stations in the epoch 2010-2019 was estimated as 18.15, 7.08, 1.30, and 1.26 ppm, respectively, and the average rate of increase in the amplitude of the seasonal variation in the increase in CO2 concentration in the interval 1976-2019 is 0.085, 0.0100, 0.0165, and 0.
In this article, we study a novel intelligent reflecting surface (IRS)-aided cognitive unmanned aerial vehicle (Co-UAV) secure communication network, in which an IRS is exploited to improve the information transmit se...
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The previous mechanical analysis on the longitudinal joint of segmental linings commonly used the plane section assumption, in which the strains on the normal section are linearly and continuously distributed. This wa...
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The previous mechanical analysis on the longitudinal joint of segmental linings commonly used the plane section assumption, in which the strains on the normal section are linearly and continuously distributed. This was proven to be overidealized according to a series of full-scale tests in existing literatures. In this paper, the nonlinear characteristics of deformation behavior for longitudinal joint were investigated with emphasis on the geometric nonlinearity of joint. A new analytical model of longitudinal joint was proposed with considering the geometric nonlinearity via employing the local plane section assumption to describe the strain distribution of joint section. The effectiveness of the proposed model was validated and parametric study (e.g. loading cycles and peak bending moment) was conducted. The results indicated the geometric nonlinearity of longitudinal joint refers to the increase area of free surface for the joint section. This nonlinearity will cause the discontinuous change of contact state which can be intuitively charactered by the height of concrete compression zone. Without considering the geometric nonlinearity, the area of concrete compression zone and bending capacity of the joint are overestimated by 45% to 100% based on the calculation result of the verification case. The increasing load cycles and peak load value deteriorate the deformation recoverability and ultimate capacity of joints. The degree of geometric nonlinearity is intensified with the increase of load cycles and peak load value but it can be alleviated if the concrete in external edge contact during overloading process.
In this paper, a new iterative algorithm is proposed for finding a common solution to a constrained convex minimization problem, a quasi-variational inclusion problem and the fixed point problem of a strictly pseudo-c...
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In this paper, a new iterative algorithm is proposed for finding a common solution to a constrained convex minimization problem, a quasi-variational inclusion problem and the fixed point problem of a strictly pseudo-contractive mapping in a real Hilbert space. It is proved that the sequence generated by the proposed algorithm converges strongly to a common solution of the three above described problems. By applying this result to some special cases, several interesting results can be obtained.
The single-index model (SIM) reveals the intricate relationship between the response variable and covariates, allowing for the consideration of potential heterogeneity and nonlinearity in the data, while also offering...
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The single-index model (SIM) reveals the intricate relationship between the response variable and covariates, allowing for the consideration of potential heterogeneity and nonlinearity in the data, while also offering interpretability and flexibility. This paper concentrates on estimating the parameters and the unknown link function for the quantile SIM with high-dimensional covariates. We introduce a novel iterative algorithm that strikes a balance between estimation accuracy, computational efficiency, and adaptability to diverse datasets. The initial values for iteration and iteration termination conditions are also discussed. The finite sample performance is illustrated through a simulation study, demonstrating the advantages of our method in terms of accuracy and speed.
PURPOSE: Total Variation (TV) minimization algorithm is a classical compressed sensing (CS) based iterative image reconstruction algorithm that can accurately reconstruct images from sparse-view projections in compute...
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PURPOSE: Total Variation (TV) minimization algorithm is a classical compressed sensing (CS) based iterative image reconstruction algorithm that can accurately reconstruct images from sparse-view projections in computed tomography (CT). However, the system matrix used in the algorithm is often too large to be stored in computer memory. The purpose of this study is to investigate a new TV algorithm based on image rotation and without system matrix to avoid the memory requirement of system matrix. METHODS: Without loss of generality, a rotation-based adaptive steepest descent-projection onto convex sets (R-ASD-POCS) algorithm is proposed and tested to solve the TV model in parallel beam CT. Specifically, simulation experiments are performed via the Shepp-Logan, FORBILD and real CT image phantoms are used to verify the inverse-crime capability of the algorithm and evaluate the sparse reconstruction capability and the noise suppression performance of the algorithm. RESULTS: Experimental results show that the algorithm can achieve inverse-crime, accurate sparse reconstruction and thus accurately reconstruct images from noisy projections. Compared with the classical ASD-POCS algorithm, the new algorithm may yield the similar image reconstruction accuracy without use of the huge system matrix, which saves the computational memory space significantly. Additionally, the results also show that R-ASD-POCS algorithm is faster than ASD-POCS. CONCLUSIONS: The proposed new algorithm can effectively solve the problem of using huge memory in large scale and iterative image reconstruction. Integrating with ASD-POCS frame, this no-system-matrix based scheme may be readily extended and applied to any iterative image reconstructions.
In this paper, the kinematics of the 3-RRPS parallel mechanism was deeply studied, and the kinematics mathematical model of the parallel mechanism was obtained by using the method of coordinate system transformation. ...
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ISBN:
(数字)9781665408530
ISBN:
(纸本)9781665408530;9781665408523
In this paper, the kinematics of the 3-RRPS parallel mechanism was deeply studied, and the kinematics mathematical model of the parallel mechanism was obtained by using the method of coordinate system transformation. The inverse solution of the parallel mechanism pose was obtained by solving this equation, and the nonlinear equation was obtained from the inverse solution. The forward solution of the attitude of the parallel mechanism was calculated by solving this nonlinear equation with Jacobian matrix and Newton iterative algorithm, which is an approximate solution. Finally, we verified the correctness of the forward kinematics solution through MATLAB.
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