231 results about "Domain transformation" patented technology

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention discloses a specific detection value identification method and device and a computer program product, and the method comprises the steps: S1, collecting the power data of a to-be-detected node of a power system, and transmitting the power data to an edge calculation layer in real time through a distributed operation system; s2, preprocessing the power data at an edge calculation layer to generate standardized time sequence data; s3, extracting time domain statistical features and frequency domain transformation features of the standardized time series data, and fusing high-dimensional features output by a pre-training deep learning model to construct a multi-dimensional feature vector; s4, performing pattern recognition on the multi-dimensional feature vector based on a pre-trained classification or clustering model, and outputting an anomaly detection result; and S5, feeding back an abnormal detection result to the power equipment control unit in real time through the distributed operating system, and triggering an alarm or executing parameter adjustment. According to the invention, efficient distribution of detection results and multi-node parallel processing can be realized, and the system performance is further optimized.

The invention relates to the technical field of defect detection, and discloses a Babbitt metal coating surfacing thin-wall layer defect detection method and device. The method comprises the following steps: carrying out multi-source detection data acquisition, multi-domain transformation and filtering enhancement on a babbitt metal coating, and obtaining preprocessed multi-source initial coating detection data to carry out interface-tissue-defect mapping, so as to obtain unified representation of an internal tissue structure of the coating; performing progressive fusion of a coating depth structure and extraction of coating defect morphology and structural features on the unified feature representation to obtain preliminary defect semantic features, and performing feature weighting calculation of defect morphology on the preliminary defect semantic features to obtain initial defect semantic features; and carrying out bearing strength and shear strength calculation and fatigue life prediction on the initial defect semantic features to obtain a final detection result. According to the method, the defects of the thin-wall layer coated and surfaced by the babbitt metal can be efficiently and accurately detected.

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention discloses a motor working condition monitoring method with a high-speed data transmission capability, and relates to the field of motor operation working condition detection and information transmission. The method comprises the following steps of: synchronously acquiring multi-dimensional time sequence data of a motor under multiple working conditions, and performing data segmentation, standardization, fixed-step downsampling, frequency domain conversion and feature fusion to form a multi-dimensional feature vector; then building a BiLSTM-Attention deep learning model, extracting bidirectional time sequence dependence of the sequence, focusing key time step features, and training to obtain a working condition monitoring model; the monitoring reliability is guaranteed through a dual detection mechanism, and high-speed synchronous transmission of multi-sensor data is realized based on an EtherCAT communication protocol stack. According to the method, through collaborative modeling of multi-dimensional time-frequency feature fusion and a deep learning model, a high-speed synchronous transmission protocol stack and a dual detection mechanism are combined, motor working condition feature expression can be remarkably enhanced, and high-precision data transmission and reliable monitoring can be achieved.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention relates to an intelligent power grid anomaly detection method and system based on a graph neural network, and the method comprises the steps: processing the data of an intelligent power grid, converting the flow data into a graph structure, and enabling the graph structure to serve as the input of a model based on a space-time graph neural network; extracting potential representation of each node in the source graph and the target graph through a shared encoder, and generating node embedding; whether the nodes on the source graph are abnormal or not is distinguished through an abnormal classifier; a domain discriminator is utilized to distinguish whether embedding of nodes is from a source graph or a target graph, so that an adversarial domain is executed to enable an encoder to be subjected to adversarial training to deceive the domain discriminator, and adversarial domain adaptation is executed in a minimum-maximum game; the difference between the source image and the target image is bridged by maximizing the loss function of the domain discriminator; after node embedding is generated by using the shared encoder, detecting the exception on the target graph through an exception classifier; by minimizing the complete loss function, the shared encoder completes domain conversion between the source map and the target map and maintains an anomaly classifier.

The invention relates to the technical field of artificial intelligence, and provides a quantum-enhanced multi-scale network intrusion detection method, which comprises the following steps: calculating a covariance matrix for an original traffic feature matrix, and obtaining a feature value and a feature vector through feature decomposition, mapping each sample xi to a quantum Hilbert space to generate an enhanced feature matrix, executing complex field transformation on the enhanced feature matrix to generate an entangled feature tensor, and realizing dynamic feature enhancement through a multi-head attention mechanism based on a quantum probability amplitude; performing space-time attention calculation and gating fusion on the feature tensor after dynamic feature enhancement to obtain a space-time fusion feature; converting the space-time fusion features into a time sequence form, extracting behavior features through a multi-scale convolution branch, and fusing the behavior features to obtain a three-dimensional feature tensor; and calculating a mean value of the three-dimensional feature tensor in a sequence dimension, generating a two-dimensional feature matrix, and performing classification prediction, uncertainty quantification and threat grading evaluation based on a classification network, an uncertainty network and a threat grading network.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The disclosure generally relates to methods and systems for graph assisted unsupervised domain adaptation for machine fault diagnosis. The present disclosure solves the technical problems in the art using a Graph Assisted Unsupervised Domain Adaptation (GA-UDA) technique for the machine fault diagnosis. The GA-UDA technique carries out the domain adaptation in two stages. In the first stage, a Class-wise maximum mean discrepancy (CMMD) loss is minimized to transform the data from both source and target domains to a shared feature space. In the second stage, the augmented transformed (projected) data from both the source and the target domains are utilized to construct a joint graph. Subsequently, the labels of target domain data are estimated through label propagation over the joint graph. The GA-UDA technique of the present disclosure helps in addressing significant distribution shift between the two domains.

The invention relates to the technical field of remote sensing image intelligent processing, in particular to a multi-scale progressive surface temperature fusion downscaling method and device, and the method comprises the steps: preprocessing a surface temperature image in a multi-scale image library; constructing a multi-scale training data set containing the surface temperature and at least one auxiliary parameter; constructing a surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training a surface temperature downscaling network and a domain transformation network based on a progressive dual-network joint training strategy; finely adjusting the downscaling model meeting a preset low-medium resolution condition; and performing low-medium-high progressive downscaling on the low-resolution surface temperature image to be processed to generate a multi-scale progressive surface temperature meeting a target high-resolution condition. The method fully considers the problem of descending scale difference of different resolutions, achieves the maintenance of the physical characteristics of the surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The invention provides an image compression artifact removal method based on a frequency domain hybrid expert network, and relates to the field of image processing. The method comprises the following steps: acquiring image data with compression artifacts; extracting shallow image features of the image data through an independent convolutional layer; extracting long-distance dependent image features of the shallow image features through a spatial attention mechanism; based on long-distance dependent image features, inputting the long-distance dependent image features into a frequency domain transformation expert network, and determining an optimal frequency domain transformation expert through parallel processing of multiple frequency domain transformation experts and an expert selection mechanism; processing the long-distance dependent image features through an optimal frequency domain transformation expert to obtain enhanced image features; performing image reconstruction on the enhanced image features to obtain a reconstructed image; the reconstructed image is an image after the compression artifacts are removed. The method is used in an image compression artifact removal process, and solves the technical problem that multiple types of complex compression artifacts are difficult to effectively remove in the prior art.

The invention relates to the field of seismic data reconstruction, in particular to a seismic data low-rank reconstruction method fusing double-domain transformation, and the method comprises the steps: obtaining original incomplete seismic data, and initializing to-be-reconstructed seismic data and other model variables and parameters under an alternating direction multiplier method frame; performing fractional order gradient transformation on seismic data to be reconstructed along different directions, and mapping to obtain fractional order gradient domain data; performing space-time Hankel tensor expansion transformation on each piece of fractional order gradient domain data, and converting the data into a space-time Hankel matrix; performing low-rank constraint on each space-time Hankel matrix by adopting a Schatten norm to obtain a seismic data low-rank reconstruction model fused with double-domain transformation; and carrying out iterative solution on the model by using an ADMM framework until the model is stable, and obtaining a final seismic data reconstruction result. The method provided by the invention can effectively reconstruct the seismic data with the missing trace, and has a reconstruction effect with strong robustness and relatively good precision for incomplete seismic data with different missing degrees.

The invention provides a face anonymous unified framework method based on knowledge distillation. The method comprises the following steps: step 1, acquiring a face image data set; step 2, constructing a human face hidden space conversion network, taking the human face image data as input, and obtaining a human face hidden code and a human face pixel space through the human face hidden space conversion network; 3, constructing a latent space feature conversion module, and obtaining a domain conversion code in a latent code space through the latent space feature conversion module by taking the face latent code as input; 4, generating an anonymous face of the human face; 5, constructing an anonymous face generation objective function; and step 6, training and testing by using the public data set, and outputting a final result. The framework fusion realizes traditional multi-domain conversion based on deep learning and other anonymous methods, and flexibly protects identity information.

The invention discloses a multivariate time sequence prediction method based on dynamic complex spectrum filtering, which comprises the following steps: acquiring multivariate time sequence data of a target scene, determining a predicted target variable and a window length, and constructing a training set, a verification set and a test set based on the data; constructing a DCSFNet prediction model, wherein the model sequentially comprises a reversible instance normalization module, a frequency domain conversion module, a dynamic complex spectrum filtering module, a time domain projection module and an inverse reversible instance normalization module; and realizing multivariate time sequence prediction by using the trained DCSFNet model. According to the method, the frequency domain features are adaptively processed through the dynamic complex spectrum filtering module, and reversible normalization and time domain mapping are combined, so that the problems that an existing method is poor in adaptability to diversified time sequence data, frequency domain information is insufficient in utilization, and prediction precision and calculation efficiency are difficult to consider at the same time are effectively solved; and multivariate time sequence prediction with high precision, high efficiency and cross-scene adaptability is realized.

The invention relates to a laser detection method, device and equipment of a laser scribing machine and a storage medium. The method comprises the following steps: carrying out laser scanning on the surface of a solar cell, collecting a reflected light intensity signal and carrying out adaptive domain transformation processing to obtain a target characteristic matrix; training a material characteristic predictor based on the target characteristic matrix, and predicting material characteristic parameters on the lineation path to obtain a material characteristic prediction value; a state space model of the dual-optical-path laser system is constructed according to the material characteristic predicted value, a Lyapunov control function is established by solving a Lyapunov equation, and constraint conditions for ensuring the stability of the system are obtained; and the constraint condition and the material characteristic prediction value are substituted into a model prediction control optimization problem, a quadratic programming problem is solved through an interior point method, and an optimal control sequence is obtained. According to the invention, accurate prediction of the material characteristics on the scribing path is realized, scribing abnormity caused by sudden change of the material characteristics is reduced, and the anti-interference capability and the control precision of the system are enhanced.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that converts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

The present invention provides a terminal and a base station in a wireless communication system, and methods executed by the terminal and the base station. The terminal comprises: a processing unit, configured to perform characteristic domain transformation on a channel matrix to obtain a transformed channel characteristic and compress the transformed channel characteristic to obtain a compressed channel characteristic; and a transmitting unit, configured to transmit the compressed channel characteristic, as feedback information, to the base station.

The invention discloses an aggregation scheduling method and device for adjustable resources, and belongs to the field of power scheduling, and the method comprises the steps: constructing a generalized energy storage model of each adjustable resource; clustering all adjustable resources according to the battery energy dissipation rate and the charging and discharging efficiency to obtain a plurality of polymers; solving a target translation factor of each adjustable resource in each polymer in each time section by taking the maximum space of the translated resource feasible region as a target and combining the transformation constraint of the feasible region; constructing a polymer feasible region constraint according to the generalized energy storage model and the target translation factor; the maximum new energy consumption is taken as a target, the polymer feasible region constraint and the power balance constraint are combined, the response power of each polymer is generated, and day-ahead scheduling is carried out according to the response power of each polymer. By implementing the method and the device, the demand response potential of the adjustable resources can be fully played, so that the problem that the response feasible interval of each resource cannot be accurately reflected when the adjustable resources are aggregated in the prior art is solved.

The invention discloses an unsupervised image conversion imaging method based on the Schrodinger bridge theory, and the method comprises the following steps: 1, constructing an image domain-domain conversion frame based on the Schrodinger bridge theory, simulating a smooth evolution path from a source domain image to a target domain image through a time condition generator, and forming antagonism training in combination with a discriminator; 2, integrating a saliency content guide constraint, a global feature consistency constraint and a contrast learning constraint in the conversion framework so as to enhance semantic content retention and detail generation capability; 3, establishing a composite loss function, and carrying out weighted combination on Schrodinger bridge path loss, adversarial loss and each auxiliary constraint loss for guiding model optimization; and 4, training a time condition generator and a discriminator through an optimization algorithm, and carrying out stable and high-fidelity target domain conversion on the source domain image by utilizing the generator after training is completed. According to the method, the stability of model training and the quality and fidelity of the generated image are remarkably improved.

The invention provides an end-to-end low-illumination image detection method based on frequency domain enhancement and related equipment. The method comprises the following steps: carrying out fast Fourier transform on a low-illumination training image by utilizing a Fourier enhancement sub-network in a frequency domain enhancement target detection network to obtain a frequency domain characteristic graph; performing complex convolution on the frequency domain feature map, continuously performing multiple times of frequency domain processing on a result of the complex convolution to obtain a frequency spectrum feature map, and performing dimensionality reduction and range constraint on the frequency spectrum feature map to obtain a second frequency domain conversion map; amplifying the amplitude of the frequency domain feature map by dividing the amplitude of the frequency domain feature map by the second frequency domain conversion map, and performing inverse fast Fourier transform by using the phase and the amplified amplitude to obtain an enhanced image; and using a preset joint loss function in the target detection sub-network to complete end-to-end training on the frequency domain enhanced target detection network.

The invention provides a dynamic adaptation method and system for a multi-mode power supply compatible charging pile system, and the method comprises the steps: building a multi-protocol physical layer compatible connection between a charging pile and a power supply module through a multi-communication interface layer, and receiving a data frame inputted by the charging pile; analyzing a baud rate, a check bit and an instruction set of the data frame, and identifying a charging pile protocol type and a power module protocol type; querying a matching mapping relationship between the charging pile protocol type and the power supply module protocol type in the dynamic adaptation matrix; if the matching mapping relation exists, data field conversion is executed, and a target protocol frame is generated; and if the matching mapping relation does not exist, starting a machine learning process, creating a temporary mapping rule, and effectively solving the problems of poor compatibility and high manual configuration cost of the charging pile and the power supply module in a traditional protocol mode through the modes of protocol analysis, intelligent matching and dynamic learning.

The invention discloses a dual-frequency-band weak target detection method based on Hough transformation, and the method comprises the following steps: S1, selecting wave beams of two frequency bands of a radar, carrying out the alignment of the wave beams of the two frequency bands in space and time, and scanning a target region; s2, acquiring echo data of wave beams of two frequency bands, and respectively setting an initial detection threshold to carry out target initial detection; s3, performing wide constraint detection on the two groups of echo data subjected to preliminary detection in the step S2 by adopting pre-installed information, and screening out detection units conforming to motion feature constraints; s4, performing dual-frequency fusion detection on the detection units screened out in the S3 based on Hough transformation, and determining a target point in a parameter domain transformation spectrum after Hough transformation; s5, for the target point determined in the S4, using Hough inverse transformation to output each dimension measurement value; the method is high in efficiency, and the detection performance is stable when the external interference noise condition changes greatly.

The invention provides a model training method and device, a target detection method and device, equipment and a storage medium. The method comprises the following steps: performing domain transformation processing on a source domain image to obtain a target domain image; and executing the following processing through the to-be-trained target detection model: extracting a first image feature of the source domain image, performing target prediction processing on the first image feature to obtain a first detection result of the source domain image, extracting a second image feature of the target domain image, and performing target prediction processing on the second image feature to obtain a second detection result of the target domain image. Obtaining a second detection result of the target domain image; constructing a first loss based on the first image feature and the second image feature, and constructing a second loss based on the first detection result and the second detection result; and updating parameters of the to-be-trained target detection model based on the first loss and the second loss to obtain a trained target detection model. According to the invention, the accuracy of target detection in the target domain can be improved.

The invention discloses a ZP-OTFS staged weight regulation MRC signal detection method, which mainly solves the problem that the number of iterations is high due to a fixed weight coefficient in the existing MRC detection algorithm, and comprises the following steps: constructing a time delay-Doppler DD domain zero-filling orthogonal time-frequency-space ZP-OTFS sending signal, and carrying out signal domain transformation on the ZP-OTFS sending signal; to generate a time domain transmit signal suitable for wireless channel transmission; the receiving end reconstructs the time domain receiving signal into a time delay-time (DT) domain receiving signal; and then MRC detection based on staged weight regulation is carried out on the DT domain receiving signal to obtain a DD domain sending signal vector estimation value. According to the method, the weight coefficient is reasonably set in different iteration stages to gradually optimize the MRC detection process, the system operation complexity can be effectively reduced, better bit error rate performance is achieved under the same complexity, and the method can be used for a ZP-OTFS system in a high-speed moving scene.

A method of coding video data, the method comprising: obtaining input data, wherein the input data includes one or more of predicted video data, reconstructed video data, quantization parameter data, boundary strength data, or prediction mode data; converting the input data from an input domain to a transform domain to generate converted video data; applying a neural network (NN)-based in-loop filter (ILF) to the converted video data to generate filtered video data; and converting the filtered video data from the transform domain to the input domain.

The invention relates to a frequency domain modeling method, device and equipment for a serial DR-MMC port and a medium, and the method comprises the steps: injecting current disturbance at the serial DR-MMC port, carrying out the measurement equivalence of a response quantity at a grid-connected point according to a preset grid-connected interface equivalent parameter set, and obtaining low-voltage side current small signals of a DR and an MMC; according to the low-voltage side current small signal, performing dual frequency domain transformation on a DR time domain switching function, establishing a DR switching function frequency domain model, and further deriving a DR AC side small signal relationship according to the switching function frequency domain model; constructing a DR frequency domain sub-model, an MMC frequency domain sub-model and a direct current frequency domain sub-model based on the relationship between the low-voltage side current small signal and the alternating current side small signal and in combination with device alternating current and direct current constraints, MMC loop control constraints and direct current network equivalent constraints; and assembling the DR frequency domain sub-model, the MMC frequency domain sub-model and the DC frequency domain sub-model at the grid connection point to obtain a frequency domain model of the AC port. The method has the effect of improving the characterization accuracy of the frequency domain characteristic of the grid-connected point port in the broadband range.

The disclosure generally relates to methods and systems for graph assisted unsupervised domain adaptation for machine fault diagnosis. The present disclosure solves the technical problems in the art using a Graph Assisted Unsupervised Domain Adaptation (GA-UDA) technique for the machine fault diagnosis. The GA-UDA technique carries out the domain adaptation in two stages. In the first stage, a Class-wise maximum mean discrepancy (CMMD) loss is minimized to transform the data from both source and target domains to a shared feature space. In the second stage, the augmented transformed (projected) data from both the source and the target domains are utilized to construct a joint graph. Subsequently, the labels of target domain data are estimated through label propagation over the joint graph. The GA-UDA technique of the present disclosure helps in addressing significant distribution shift between the two domains.

In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.