5 results about "Exponential form" patented technology

Method for the automatic detection of a thermal anomaly in equipment, associated device. The present invention relates to a method for the automatic detection of a thermal anomaly in equipment, comprising the steps of: calculating the temperature T(t) of the equipment towards which it tends in the vicinity of , verifying, by local extrapolation of T(t) in exponential form: ; predicting an asymptotic equilibrium temperature , as a function of usage variables, characterizing the use and context of use of the equipment at time t; determining, as a function of , the presence of a thermal anomaly in the equipment. Figure for the abstract: Figure 1

The invention relates to a slope displacement prediction method based on an IMVO-RELM model, and belongs to the technical field of slope engineering. The method comprises the following steps: collecting slope displacement data, dividing a training set and a test set, and normalizing the data of the training set and the test set; in order to improve the search capability and convergence of a multivariate universe (MVO) algorithm in the RELM parameter optimization process, the MVO algorithm is improved, spiral updating and adaptive compression factors are introduced, the wormhole existence probability (WEP) and the travel distance rate (TDR) of a traditional multivariate universe optimization algorithm are improved in an exponential form, and an IMVO algorithm is obtained; the IMVO algorithm is used to optimize the parameters of the RELM, and an IMVO-RELM slope displacement prediction model is constructed; training the IMVO-RELM slope displacement prediction model by using the data of the training set to obtain an optimal training model; and inputting the data of the test set into the optimal training model for slope displacement prediction, and evaluating a slope displacement prediction result by using RMSE, MAPE and R2.

The invention provides a signal strength determination method and device, electronic equipment and a medium, and the method comprises the steps: obtaining a wireless communication signal represented in a logarithmic form, converting the wireless communication signal into an exponential expression form, and obtaining an exponential function with a base number in the wireless communication signal as a base; decomposing an exponent in the exponential function into an integer part and a decimal part, and dividing the exponential function into an integer function of which the exponent is the integer part and a decimal function of which the exponent is the decimal part; searching a function value corresponding to the decimal function based on a preset table; and determining the target signal strength of the wireless communication signal based on the function value of the integer function and the function value of the decimal function.

The invention discloses a super-exponential convergence zero neural network method for position tracking of a redundant mechanical arm, and belongs to the technical field of robot motion control and intelligent computing. A zero neural network fusing a time-varying dynamic attenuation coefficient and a power-sigmoid type nonlinear activation function is constructed, and by designing an error evolution kinetic equation based on a Jacobian matrix Moore-Penrose pseudo-inverse, a network state is driven to rapidly converge to a time-varying pseudo-inverse truth value in a super-exponential form. And the output of the network serves as a pseudo-inverse online estimated value, a joint speed instruction can be generated in real time in combination with tail end position tracking error feedback, and high-precision, rapid and stable tracking of the tail end of the redundant mechanical arm to an expected trajectory is achieved. The method only depends on a kinematic model, does not need complex dynamic modeling or offline training, has the advantages of being simple in structure, small in calculation amount and high in real-time performance, and is suitable for tracking control scenes with different structural parameters and various complex trajectories.

The application is a power distribution network random power flow acceleration algorithm based on inter-regional scene equivalence, relates to the field of random power flow algorithm, and comprises the following steps: a multi-regional power flow model that influences each other through boundary quantity is established, an inter-regional scene equivalence method is proposed, and a distributed solution algorithm of random power flow is designed. The application considers the problem that a huge calculation burden is caused by a large number of simulations on node injection power in the random power flow algorithm, constructs a multi-regional power flow model that influences each other through boundary quantity, proposes an inter-regional scene equivalence method, and reduces redundant scene information in the information transmission process. Further, a distributed solution algorithm of random power flow is designed on the basis, reduces the scene number of the traditional centralized type in an exponential form, can solve more scenes under the same time constraint, and avoids the problem of too large operation quantity.