11 results about "Density evolution" patented technology

The application relates to a channel code optimization method based on a Gaussian weighted mixed approximate density evolution, and relates to the technical field of wireless communication. The method comprises the following steps: constructing a modulation system; introducing a delay module at a sending end, performing joint source and channel decoding at a receiving end, and feeding back the likelihood information of the delay bits to a demodulator; obtaining the probability distribution of the likelihood information of the outputs of each subchannel of the system, and confirming that the distribution does not satisfy a symmetric Gaussian distribution; based on the characteristics of the likelihood information of the outputs of each subchannel under the condition of a single constellation point, using a Gaussian weighted mixed approximate density evolution algorithm to analyze the decoding convergence performance, and obtaining the Gaussian weighted mixed approximate distribution of the likelihood information of the outputs of each subchannel; using the Gaussian weighted mixed approximate density evolution algorithm as an analysis tool to search and optimize, and obtaining an optimized channel code base matrix; using the optimized channel code base matrix to construct a channel encoder, and replacing the original channel code to improve the bit error rate performance of the system in a waterfall region.

The present application relates to a kind of multi-agent collaborative induction and conflict solution method and system based on probability density evolution and wasserstein gradient flow.The method is mapped into continuous probability density field by stratified anisotropic kernel density estimation to the discrete position of multi-agent, constructs control free energy functional in probability space, which fuses target attraction, obstacle repulsion, collision avoidance interaction and Fisher information smoothing regularization term;Based on optimal transport and variational method, derive the macroscopic velocity field that enables the fastest decline of energy, and map into individual control instruction that meets the dynamic constraint.The scheme can improve the safety, smoothness and convergence of cluster flight, and is suitable for unmanned aerial vehicle cluster control and urban low-altitude traffic management.

The application discloses a nonlinear random structure dynamic reliability calculation method, and belongs to the field of structure health monitoring, comprising the following steps: collecting structure dynamic response data, and extracting high-order component signals of the structure dynamic response; correcting a random structure nonlinear model of an actual structure, and determining the probability distribution of the random structure nonlinear model parameters; applying external excitation load to the random structure nonlinear model, establishing a nonlinear random structure dynamic response state equation, and obtaining a probability density evolution equation; applying an absorbing boundary condition to the structure dynamic response according to a first exceeding failure criterion, combining a finite difference method and a nonlinear time-history integration method, obtaining the structure dynamic reliability changing with time under the dynamic load, and realizing the quantification of the reliability evolution process of the actual structure under the external load, avoiding the response assumption in the traditional reliability calculation process, and improving the calculation precision of the structure reliability.

The invention discloses a wing structure dynamics uncertainty solving method based on a symplectic characteristic line method, and belongs to the technical field of structural dynamics uncertainty analysis. The method comprises the following steps: firstly, establishing a cantilever beam model of a wing structure, determining uncertain parameters, and constructing a generalized probability density evolution equation; selecting a representative sample point according to the parameter probability distribution and calculating an assigned probability; thirdly, converting a structural kinetic equation into a Hamiltonian system form, and gradually solving a characteristic line by utilizing a characteristic line method of a hyperbolic equation and combining a symplectic algorithm; and translating the initial condition along the feature line to obtain the probability density distribution of each sample point, and finally superposing all sample point results to obtain a probability density function of the system response. According to the method, the feature line solving precision is improved by introducing the symplectic algorithm, the characteristics of the Hamiltonian system are better maintained, and the precision of dynamic uncertainty analysis of the wing structure is effectively improved.

The invention discloses an anti-seismic reliability analysis method, device and system for a self-resetting assembled pier, and a storage medium. The method comprises the following steps: step 1, determining a basic random variable of a structure; 2, selecting a representative sample, and calculating an assignment probability of a probability subspace; step 3, structural random response analysis; 4, carrying out structure hysteresis analysis; 5, calculating a damage index and an evolution speed of each sample; step 6, defining a damage criterion of the self-resetting segment prefabricated assembled pier; 7, solving a generalized probability density evolution equation with an absorption boundary; step 8, obtaining a'residual 'probability density function; and step 9, evaluating the time-varying anti-seismic reliability of the self-resetting segment prefabricated assembled pier. By adopting the technical scheme of the invention, scientific evaluation of the anti-seismic safety performance of the self-resetting segment prefabricated assembled pier under the consideration of the influence of material randomness is realized.

The present application relates to a kind of vehicle-bridge coupling system reliability analysis method based on adaptive multi-task deep learning, belong to rail transit infrastructure safety technical field.For the problem that existing technology cannot efficiently and synchronously predict vehicle and bridge response, multi-task loss weight adjustment is difficult, adaptive multi-task learning model AMLM is proposed.The model integrates improved gorilla group optimization algorithm MGTO, shared convolutional neural network CNN and double-path state fusion gated long short-term memory network SFLSTM;Through the same variance uncertainty loss weighting strategy, the vehicle and bridge prediction tasks are dynamically balanced;Combined with probability density evolution method PDEM, AMLM-PDEM framework is constructed.The technical effects include: realizing synchronous high-precision prediction of vehicle-bridge response, autonomously optimizing multi-task weight distribution, significantly improving calculation efficiency and modeling accuracy, and perfecting the time-varying reliability evaluation system of vehicle-bridge system.

The invention relates to the technical field of unmanned aerial vehicle communication, particularly discloses a polarization code construction method suitable for a Rician fading channel, and provides two low-complexity polarization code construction methods suitable for a fast Rician fading channel, the first method is a simplified discrete density evolution (DDE) method, and the second method is a low-complexity polarization code construction method suitable for a fast Rician fading channel. According to the method, a probability density function PDF of a received symbol log-likelihood ratio LLR is discretized into a probability quality function PMF, optimization is achieved through PMF evolution based on minimum sum decoding, a numerical result shows that the proposed DDE method can effectively evaluate the polarization code performance and can also be used as a practical construction tool, and the method is suitable for popularization and application. According to the second method, a Rice channel is approximated to be a binary input additive Gaussian white noise B-AWGN channel with equivalent capacity, a Gaussian approximation density evolution (DEGA) method is adopted for optimization, the simulation result shows that the performance of the polarization code constructed by the Gaussian equivalent GE method is equivalent to that of the polarization code constructed by the DDE method, and meanwhile, the construction complexity is reduced.

The invention discloses a channel code pattern optimization method based on Gaussian weighted hybrid approximate density evolution, and relates to the technical field of wireless communication. The method includes constructing a modulation system. A delay module is introduced at a sending end, joint information source and channel decoding is carried out at a receiving end, and likelihood information of delay bits is fed back to a demodulator. Likelihood information probability distribution output by all sub-channels of the system is obtained, and it is confirmed that the distribution does not meet symmetric Gaussian distribution. Based on the characteristics of likelihood information output by each sub-channel under the condition of a single constellation point, decoding convergence performance is analyzed by adopting a density evolutionary algorithm of Gaussian weighted hybrid approximation, and Gaussian weighted hybrid approximation distribution of the likelihood information output by each sub-channel is obtained. And searching and optimizing by using a density evolutionary algorithm based on Gaussian weighted hybrid approximation as an analysis tool to obtain an optimized channel code basis matrix. And constructing a channel encoder by using the optimized channel code basis matrix to replace the original channel code so as to improve the bit error rate performance of the system in the waterfall region.

This invention discloses a sequential reliability design method for PID controllers considering stochastic and interval-specific uncertainties. This method targets PID (proportional-integral-derivative) closed-loop control systems containing both stochastic and interval-specific uncertainties. It calculates the mixed reliability using probability density evolution equations and an adaptive bisection method, then constructs an optimization formula for the PID gain that minimizes the control force index using the mixed reliability as a constraint, and solves the optimization formula using a sequential optimization strategy. This method describes the uncertain variables stochastically and in intervals, calculates the mixed reliability using probability density evolution equations and an adaptive bisection method, and finally designs a PID controller with the minimum control force index using the mixed reliability as a constraint through a sequential optimization strategy. This invention can be used for controller design in PID closed-loop systems with multiple mixed uncertainties, some of which have limited probabilistic information.

This invention discloses a method and system for structural reliability analysis of a high-rise building machine based on active learning. The method includes: establishing a stochastic pulsating wind field to simulate pulsating wind loads on a high-rise building and creating a sample pool; establishing a finite element model of the high-rise building machine and assigning material properties; training an AI agent model based on the sample pool and the finite element model, using an improved QBDC active learning approach; predicting the time-history response of all samples in the sample pool using the trained AI agent model; and determining the structural dynamic reliability based on the prediction results and the PDEM algorithm. Addressing the limitations of traditional reliability analysis methods, such as narrow applicability, large workload, and time-consuming computation, this invention combines artificial intelligence algorithms, finite element method (FEM), and probability density evolution method (PDEM) to achieve rapid construction of a stochastic excitation-structural response AI agent model, thereby overcoming the shortcomings of existing methods in structural reliability assessment.

The invention provides a tailing dam time-varying reliability analysis method considering freeze-thaw degradation and earthquake randomness. The method comprises the following steps: acquiring shear strength parameters and permeability coefficient statistical characteristics of a tailing material in a freeze-thaw environment; constructing a physical random seismic motion model, and obtaining a plurality of random seismic motion parameters; according to shear strength parameters, permeability coefficient statistical characteristics and the random seismic oscillation parameters, generating random samples and corresponding assigned probabilities based on a probability space subdivision method of grouping-score deviation; establishing a tailing dam finite element model, and calculating a slope safety coefficient corresponding to each group of samples based on unsaturated seepage analysis and slope stability analysis; based on a probability density evolution theory, establishing and solving a generalized probability density evolution equation, and obtaining a probability density function of a slope safety coefficient and a time-varying evolution process of the probability density function; and solving the failure probability and reliability index of the tailing dam at different moments. The accuracy of reliability evaluation of the tailing dam is effectively improved.