7 results about "Nonlinear neural networks" patented technology

The invention relates to the technical field of software security and program protection, in particular to a code obfuscation method based on a neural arithmetic unit, which comprises the following steps: step 1, code acquisition and operation verification; step 2, code deep analysis and detailed solution; step 3, model construction and training export; step 4, extracting and confirming confusion parameters; 5, generating and replacing obfuscated codes; step 6, running reasoning and verification; according to the method, an original arithmetic instruction in a program is replaced by a neural arithmetic unit subjected to constraint training, original clear arithmetic semantics are completely embedded into a high-dimensional and nonlinear neural network parameter space, and an attacker faces confused codes, so that the algorithm semantics are completely embedded into the high-dimensional and nonlinear neural network parameter space; the original calculation intention cannot be recovered through the traditional algebraic simplification or logical reasoning means, and the essential improvement from grammar confusion to calculation semantic confusion constructs a deeper defense barrier, and can effectively cope with various advanced reverse analysis technologies including intelligent decompilation.

Various example embodiments relate to mitigation of a non-linearity in a data communication chain. A method may include: obtaining a data communication signal including a plurality of complex-valued input samples; capturing, from the plurality of complex-valued input samples, a current sample and a set of delayed samples; generating a phase-normalized input signal based on normalizing phase of the current sample and the set of delayed samples by a normalization term, wherein the normalization term is common for the current sample and the set of delayed samples; providing the phase-normalized input signal to a neural network configured to mitigate non-linearity of a data communication chain and to output a complex-valued output sample for each of the plurality of complex-valued input samples; and denormalizing phase of the complex-valued output sample by a denormalization term configured to restore phase of the data communication signal.

This invention provides a method and system for analyzing the safe operation of grid-connected offshore wind farms. The method includes: acquiring the network topology and operating parameters of the grid-connected offshore wind farm to construct a power flow feasible domain injection space and a node admittance matrix; obtaining the power flow feasible domain based on these two parameters to determine stability criteria; generating an operating sample set by determining the active power injection space of the grid-connected offshore wind system and the number of training samples; and combining the operating sample set and stability criteria to obtain a stability margin set, which is then used to train a margin prediction model under a nonlinear neural network for hyperparameter selection and early shutdown strategy training. Finally, the active power injection space of the grid-connected offshore wind system is used to obtain an operating condition set under the power flow feasible domain, and a stability margin prediction set is obtained under the margin prediction model to construct the low-frequency oscillation safety domain boundary of the grid-connected offshore wind system for safe operation analysis. This invention can improve the accuracy of safe operation analysis of grid-connected offshore wind farms.

The present disclosure provides methods and systems for determining information of a sample location. One system includes a computer subsystem configured to input one or more images of a sample location into a zone-based neural network configured to perform digital nonlinear filtering on the one or more images to thereby generate filtered images of the sample location. The computer subsystem is further configured to determine information of the sample location from the filtered images.

The application provides a learning-based tomographic imaging and reconstruction method to measure the scene density distribution in a light reuse manner. During imaging, the light source emits light according to the intensity obtained through pre-learning, the light from different directions reaches the sensor after being absorbed and attenuated by the scene, and the density information of the scene is obtained by calculating and reconstructing the measurement value. The light emission intensity and the reconstruction algorithm are obtained by learning through a neural network. The method models the CT imaging process as a linear fully connected layer, and the weight corresponds to the light emission intensity of the light source during imaging; the reconstruction algorithm is modeled as a nonlinear neural network, which can be optimized according to the characteristics of the scanning geometry. The method requires a small amount of collected data, and the calculation and reconstruction do not require strong prior assumptions, realizes efficient and high-quality CT acquisition and reconstruction, and can be applied to high-speed dynamic scene three-dimensional reconstruction.

The present application relates to a kind of all-optical nonlinear neural network systems based on linear system, belong to optical neural network technical field.Solve the bottleneck problem that optical neural network relies on nonlinear material or photoelectric conversion to realize nonlinear operation.Techinical scheme includes input module, and input signal is loaded in annular resonant cavity resonance wavelength detuning amount;Coupling module controls interlayer weight;Neuron module passes through multilayer annular resonant cavity linear transmission signal;Output module loads output signal in probe light power.Input and output signal are loaded in different physical quantities, so that linear system realizes nonlinear function.Affordable effect includes breaking through optical nonlinear operation bottleneck, significantly reduce system complexity and energy consumption, enhance compatibility and practicality, improve parallel computing efficiency and system scalability.