23 results about "High dimensional" patented technology

The invention belongs to the technical field of powder metallurgy, and particularly relates to a high-precision injection-molded titanium alloy workpiece and a machine learning assisted preparation method thereof.The machine learning assisted preparation method of the high-precision injection-molded titanium alloy workpiece comprises the steps that an original data set is obtained through three-dimensional mold filling numerical simulation, performing data processing on data in the original data set to obtain an expanded data set; based on the expanded data set, multiple different machine learning algorithms are adopted to construct an injection molding process parameter prediction model, and an optimal prediction model is obtained; and optimal process parameters are obtained based on the model, and a target titanium alloy workpiece is prepared. According to the method provided by the invention, three-dimensional mold filling numerical simulation and machine learning are fused, and the process parameter prediction model is constructed and optimized, so that the actual MIM process is guided, the titanium alloy workpiece with high dimensional precision can be obtained, the research and development period is greatly shortened, and the trial-manufacturing cost is reduced.

The invention discloses a wheel forging and rolling composite forming process based on deformation distribution and a structure regulation and control method, and belongs to the technical field of wheel manufacturing. The method comprises the steps that a composite forming path of pre-forging, finish forging and finish rolling is adopted, the deformation amount of each procedure is scientifically distributed, pre-forging accounts for 70%-80%, finish forging accounts for 15%-20%, and finish rolling accounts for 5%-10%, so that forming precision and microstructure control is achieved. Meanwhile, quantitative prediction is carried out on microstructure evolution in the whole forming process by constructing a multi-scale digital simulation platform integrating a material constitutive model, a microstructure evolution model and a cellular automaton. And based on a prediction result, active and accurate regulation and control of microstructures of different parts of the wheel are realized through virtual iteration optimization of key process parameters such as temperature, deformation and strain rate, and finally, the complex-structure wheel with high dimensional precision, uniform and fine structure and excellent comprehensive performance is obtained. According to the wheel shape-character collaborative manufacturing method, shape-character collaborative manufacturing of the wheel is achieved, and the scientificity and efficiency of process design are improved.

This relationship visualization device comprises: a relationship information acquisition unit 1 that acquires relationship information representing a relationship between a central person and another person other than the central person; and a display unit 2 that displays the position of the other person in a predetermined two- or higher dimensional coordinate system determined on the basis of the relationship information.

A system and method for Medical Claims Risk Score (MCRS) algorithmic underwriting includes a predictive machine learning model configured to generate underwriting decisions on electronic applications. MCRS underwriting applies word embedding modeling, such as GloVe (global vectors), to transform high dimensional MC records into single-code word vectors. These single-code word vectors are employed in regression modeling, and may include summarized embedding coordinates aggregated at the applicant level. Regression modeling uses medical claim codes data and underwriting decision data stored for historical underwriting applicants to train a random forest model to predict relative mortality risk for underwriting applicants. A risk rating may be derived from the underwriting decision data based upon standard quantitative risk ratings of a plurality of risk classes. Other inputs to the random forest model may include cohort level applicant profile data, such as applicant issue age and sex.

In some implementations, a device may obtain a training corpus, from a set of pre-processed log data, associated with an alphanumeric format. The device may encode the training corpus to obtain encoded data using a set of tokens. The device may calculate a sequence length based on a statistical parameter associated with the training corpus. The device may generate a set of input sequences and a set of target sequences based on the encoded data, where each input sequence and each target sequence has a length equal to the sequence length. The device may generate a training data set based on combining the set of input sequences and the set of target sequences. The device may train a deep neural network (DNN) using the training data set and based on one or more hyperparameters to obtain a set of embedding tensors associated with an embedding layer of the DNN.

The application discloses a kind of heterogeneous multi-view enhancement subsequence unit learning session recommendation method, specifically related to the technical field based on session recommendation, solve the interaction between single item in the prior art, lack rich global context information, it is difficult to understand the intention of user from higher dimensional angle technical problem;Its technical scheme is: multiple continuous items are regarded as a subsequence unit, and learning is carried out on the subsequence level;It explores the intention of user within a certain range by subsequence, but not just focus on the direct relationship between items;The number of items in subsequence can be dynamically adjusted, so as to explore the influence of subsequence of different lengths on recommendation performance;The application can apply subsequence unit learning user intention in multiple sessions, better capture the context information in global session.

This disclosure relates generally to method to determine an optimal set of atom centered symmetry functions. One or more parameters associated with one or more atom centered symmetry functions (ACSFs) are received. An initial set of ACSFs is generated by varying the one or more parameters. A histogram with a prespecified bin size is constructed to obtain a distribution of value of each of the initial set of ACSFs. A pruned list of ACSFs is obtained based on width and maximum value of the distribution of the value of initial set of ACSFs. The pruned list of ACSFs is sorted in decreasing order of spread to obtain a sorted list of ACSFs. An optimal set of one or more shortlisted ACSFs is determined by traversing through the sorted list of ACSFs. A high dimensional neural network potential is trained based on the optimal set of one or more shortlisted ACSFs.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

The application discloses a kind of based on layered Gaussian model's electromagnetic relay multi-fidelity transfer agent modeling method, the method uses three-layer Gaussian process model of embedded ARD kernel function as modeling method, relies on the multi-source fusion data of physical mechanism-history product data-finite element simulation-target product data, realizes the multi-fidelity small sample transfer agent model establishment of electromagnetic relay.This method introduces transfer learning, while considering the calculation limitation of finite element simulation under medium-high dimensional conditions, greatly reduces the agent model training cost of multiple electromagnetic relays with the same structure, while maintaining good calculation accuracy.The method of the application generally requires less than 10% of the ordinary model under the condition that the model accuracy and ordinary Gaussian process regression agent model are flat, and the model is more applicable.

A computer-implemented method of predicting a remaining useful life of a device or a component thereof is disclosed, the method comprising: receiving high dimensional data corresponding to operating parameters of the device or a component thereof; converting the high-dimensional data into health indicator data, wherein the health indicator data comprises low-dimensional data indicating the health state of the equipment or the components thereof; determining a temporal relationship within the health indicator data to obtain health indicator trajectory data; receiving historical maintenance data of the equipment or components thereof; determining a probability of failure of the device or a component thereof based at least in part on the historical maintenance data and the health indicator trajectory data; and predicting the remaining useful life of the device or a component thereof based at least in part on the probability of failure.

A method for high dimensional spatial analysis includes segmenting, into a plurality of segments, an image depicting a plurality of cells comprising a biological sample. Each segment of the plurality of segments may correspond to one cell of the plurality of cells. A phenotype for each cell of the plurality of cells depicted in the image may be determined based on the segmented image. The determining of the phenotype may include identifying, within the plurality of cells, a first cell type having a first phenotype and a second cell type having a second phenotype. One or more metrics, such as a colocation quotient or an Earth Mover's Distance, quantifying a co-occurrence pattern between the first cell type and the second cell type may be determined. A visual representation of the co-occurrence pattern between the first cell type and the second cell type may be generated based on the metric.

The application discloses a kind of based on comprehensive data analysis's software user experience evaluation method and system, it is related to human-computer interaction technical field, this method is by embedding behavior perception engine in software operation interface, collect the use frequency of user in task path, jump operation frequency, process standard step number and access user total number, and further unified normalization processing is carried out in background server, constructs normalized behavior dataset, calculates path deviation joint index PDI.In based on path deviation joint index PDI joint consideration path selection information entropy and path jump rate mean two dimensions, whether the use of user path exists concentrated deviation or process jump behavior can be effectively identified, and then whether there is forced guidance, process hidden shortcut in system path design is judged.Compared with the evaluation mode of traditional only relying on click heat map or jump rate, the present method realizes higher dimensional path behavior modeling and deviation identification ability.

The application discloses a data aggregation method based on multi-modal features, comprising: collecting multi-modal data, pre-processing, extracting features according to modes and dividing into high, medium and low dimensional data, for high dimensional data, using ball tree algorithm to locate the near neighbor point; medium dimensional data is based on distribution density to dynamically adjust the neighborhood range; the low dimensional data is calculated by the Euclidean distance, and then is mapped to the low dimensional space by the aid of the local linear embedding, then traverses the low dimensional data, and the discrete data value frequency and the continuous data probability density are counted, the marginal probability is calculated by combining the information entropy, and the data aggregation weight is determined, finally, the features after dimension reduction are spliced in the order of high, medium and low levels, the probability normalization is carried out in each level block, and the aggregated comprehensive features are generated. The method realizes the aggregation of multi-modal features through multi-dimensional differentiated processing and weight calculation based on data distribution, and improves the feature complementarity and accuracy.

The present disclosure relates to methods and systems for training and utilizing a machine-learning model with a Deep Kernel Learning with Gaussian processes (DKL-GP) architecture to handle datasets with missing values. The system can receive a dataset with incomplete data, identify missing values, and process the dataset using the DKL-GP architecture. This can involve generating latent variables, utilizing inducing variables to approximate a Gaussian process, and mapping the latent variables to output predictions with associated uncertainty estimates. The system can optimize model parameters through a training process that leverages Pólya-Gamma data augmentation and Gaussian process inducing points for efficient computation. The trained model can subsequently be used to generate predictions for data records with missing data values, while obviating the need to impute potential values for the missing values, and make decisions based on the predictions.

A system, method and computer product for processing tensor data comprising the steps of: receiving weight tensor data from a memory bank; storing the weight tensor data in a weight tensor buffer; receiving feature map data from the memory bank; storing the feature map data in an FVC buffer; broadcasting a portion of the weight tensor data; receiving and processing the portion of weight tensor data with one or more computing units; transferring a portion of the feature map data to the one or more computing units; receiving and processing the portion of feature map data in the one or more computing units; performing elementwise multiplication operation of the weight tensor data and feature map data; summing a result of the elementwise multiplication operation of the weight tensor data and feature map data; and storing a result of the summation in an accumulator.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

The present invention provides a system (200) for reducing a number of testings for a high-dimensional assay for detecting, identifying, and quantifying a plurality of analytes in a plurality of biological samples. The system is configured to (i) generate a pooling matrix for pooling and testing the plurality of biological samples, (ii) obtain an output data on completing the high-dimensional assay in each of the plurality of pools, (iii) generate a set of linear equations based on the output data and the generated pooling matrix, and (iv) convert the set of linear equations using a compressed sensing algorithm and at least one regularity condition to detect, identify, and quantify the plurality of analytes in the plurality of biological samples. The regulatory condition is sparsity with respect to a presence or an absence of each analyte separately, or a disproportionate number of samples having disproportionately high values for a particular analyte.

A processor-implemented method and system for generating a high-definition (HD) map for high-precision position estimation and map maintenance is provided. The method includes mapping an input red-green-blue (RGB) data into a latent space vector representing an image location and an orientation in a three-dimensional (3D) space. The method further includes mapping the image location and the orientation of the input RGB data to a high-dimensional synthesized RGB image. The method further includes acting as a discriminator in a generative adversarial setup by utilizing real-time RGB image data and the high dimensional synthesized RGB image based on the image location for determining whether the HD map needs to be updated. The method further includes operating as a convolution neural network (CNN) binary classifier, for determining whether the input RGB image should be included in a stack for an offline model retraining.

The present disclosure relates to a system for predicting epileptic seizures, the system comprising: an input interface configured to receive electroencephalogram signals, wherein the electroencephalogram signals represent electrical activity of a brain of a subject; a spike encoder operatively coupled to the input interface, the spike encoded configured to convert the electroencephalogram signals into a series of spikes; a spiking neural network operatively coupled to the spike encoder, the spiking neural network configured to process the series of spikes to identify indicators predictive of an epileptic seizure, wherein the spiking neural network comprises: a recurrent layer configured to project the series of spikes into spike data of a higher dimensional space;a readout layer configured to identify the indicators predictive of an epileptic seizure, wherein the readout layer comprises a reduced set of readout neurons trained to distinguish between different pre-ictal activities based on the spike data of the higher dimensional space; an output interface configured to, directly or indirectly, communicate the indicators predictive of an epileptic seizure. The disclosure further relates to a computer-implemented method for processing electroencephalogram signals representing electrical activity of a brain of a subject and to a computer- implemented method executed on a computing device for training a spiking neural network for processing physiological electroencephalogram signals.

The invention relates to the technical field of variable selection, and discloses an ultra-high-dimensional data model-free variable selection method based on sufficient dimension reduction, which comprises the following steps of: searching an optimal regularization parameter for each column of an SAVE sufficient dimension reduction space, performing Lasso estimation on each column of the SAVE sufficient dimension reduction space to obtain an estimated matrix, and selecting the optimal regularization parameter for each column of the SAVE sufficient dimension reduction space; norms of all rows of the metabolite of the subject in the corrected matrix are extracted and arranged from large to small according to the norms, and a previous effective variable is obtained. According to the method, the difference between a metabolite covariance matrix and a slice covariance matrix is calculated through a full sample, signals of core effective variables are converted into features with extremely high identification degree in a U matrix, a preposition is stabilized on a lithotripsy map, interference of noise and dimension expansion is avoided, and a foundation is laid for TPR improvement and FPR reduction.

Methods, systems and devices are provided for utilizing user movement data obtained from one or more wearable sensors during physical activity to compare individualized changes overtime, for example typical versus atypical movement patterns, with subgroup analyses for assessing changes between other users in order to develop an assessment of movement for, for example, tracking injury risk, performance, and / or rehabilitation. The movement information may comprise multi-sensor, high dimensional datasets. Techniques are provided for integrating human movement data from one or more wearable sensor with one or more additional data sources to define an individualized movement profile of a user's movements. The user or another individual may be notified when the user's movements deviate from this individualized movement profile.

To provide a faucet device with higher dimensional accuracy. [Solution] The faucet device 1 of the present invention comprises a faucet body 2, a wall fixing member 32 for fixing the faucet body 2 to a wall, an outer casing member 26 that forms the outer casing of the faucet body 2, a water-permeable member 28 provided inside the outer casing member 26, connecting members 34, 50 that connect the wall fixing member 32 and the outer casing member 28 along the longitudinal direction, and water pressure transmission members 36, 52 that are inserted into the gap between the connecting members 34, 50 and the water-permeable member 28 and transmit the water pressure acting on the water-permeable member 28 to the connecting members 34, 50.

The application discloses an image classification method based on important parameter constraint continuous learning, and belongs to the field of image processing. It is found that in a very high dimensional parameter space, even if the LoRA module is constrained to be orthogonal, it may also lead to a suboptimal solution, so that the model cannot completely alleviate the forgetting problem. In the application, (1) it is found that even under the orthogonal LoRA constraint condition, the model parameters sensitive to the loss of historical tasks will still change significantly on each task; (2) the importance of each trainable model parameter in the current task is defined according to the sensitivity of the model parameter to the loss; (3) based on the importance of the model parameter, an important parameter constraint method is proposed, which effectively alleviates the forgetting of the model to the knowledge of the current task by constraining the parameters of the model of the current task from changing in subsequent tasks. Experiments prove that the method proposed in the application exhibits excellent performance on multiple continuous learning data sets.