Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.

A target speech signal extraction method for robust speech recognition includes: initializing a steering vector for a target speech source and an adaptive vector, setting a real output channel of the target speech source as an output by the adaptive vector, initializing adaptive vectors for a noise and setting a dummy channel as an output by the adaptive vectors for the noise; setting a cost function for minimizing dependency between a real output for the target speech source and a dummy output for the noise; setting an auxiliary function to the cost function, and updating the adaptive vector for the target speech source and the adaptive vectors for the noise by using the auxiliary function and the steering vector; estimating the target speech signal by using the adaptive vector thereby extracting the target speech signal from the input signals; and updating the steering vector for the target speech source.

A current signal segmentation algorithm is provided. The segmentation algorithm divides a current signal waveform into mutually different segments according to a physical feature thereof, extracts shape distribution, statistical and harmonic features of the segments, and calculates a similarity between a segment pair. The segmentation algorithm includes the following steps: segmenting a current signal to separate a standby current and an overshoot current, only leaving a working current; extracting a shape distribution feature of a working current segment; extracting a statistical feature of the working current segment; extracting a harmonic feature of the working current segment; calculating a similarity between a segment pair; and deriving a maximum clique set in a similarity graph through a maximum clique search algorithm as a class from automatic segmentation. The algorithm can quickly and accurately classify current signals generated by different electrical appliances in different working states so as to facilitate subsequent processing.

A method (10) of and a device (50) for performing an operation based on sensor signal data obtained from at least one sensor. From the sensor signal data, by a processor (54) of the device (50), a feature profile is generated that is matched (12) to a set of predetermined feature profiles. The operation is performed (13) by the device (50) based on the sensor signal data, if the generated feature profile matches at least one of the set of predetermined feature profiles, and the operation is performed (14) by the device (50) based on remote processing (40) of the sensor signal data (40) if the generated feature profile does not match at least one of the set of predetermined feature profiles. With the disclosed method, balance in processing power, processing time, and reliable operation by the device (50) is achieved without incurring extra cost for upgrading the device (50).

A method for predicting a clamp force using a convolutional neural network includes: generating a cepstrum image from a signal processing analysis apparatus; extracting a characteristic image by multiplying a predetermined weight value to pixels of the generated cepstrum image through artificial intelligence learning; extracting, as a representative image, the largest pixel from the extracted characteristic image; synthesizing an image by synthesizing the extracted representative image information; and predicting a clamp force by comparing the synthesized image with a predetermined value.

Provided is a method for detecting a driver's abnormalities based on a CAN (Controller Area Network) bus network communicating with an ECU (Electronic Control Unit) of a vehicle. The method may include: acquiring a CAN bus signal related to an operation of the vehicle from the CAN bus network; extracting a detection vector from the CAN bus signal using an auto encoder; and detecting a driver's abnormality based on the detection vector.

There is provided a system and method of time series (TS) prediction. The method includes providing a machine learning (ML) network trained to perform TS prediction with respect to one or more components, the ML network configured with a set of hyperparameters including one or more hyperparameters associated with each component, the ML network comprising one or more ML modules operatively connected to an output layer, each ML module configured to represent a respective component in accordance with a given model characterized by the one or more hyperparameters associated therewith, where values of the hyperparameters associated with each component are automatically optimized during training of the ML network; and in response to a user's request for TS prediction, using the trained ML network to perform TS prediction, giving rise to a prediction result comprising an overall predicted TS, and one or more decomposed TS corresponding to respective components.

A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.

A sensing system including multiple sensor node devices and an analysis device, wherein: each of the multiple sensor node devices has a sensor that measures a measurement target and acquires data values, a learning unit that, based on the data values, learns a model used to estimate the data values at an installation position of the sensor, and a communication unit that transmits learning result data indicating a learning result from the learning unit; and the analysis device has a spatial analysis unit that estimates a spatial distribution of the data values based on the learning result data from each of the multiple sensor node devices and the installation positions of the respective sensor node devices.

According to one or more embodiments, operations may comprise obtaining a first point cloud. The operations also comprise performing segmentation of the first point cloud, the segmentation generating one or more clusters of points of the point cloud. The operations also comprise determining, for each respective cluster of the plurality of clusters, a respective geometric feature of a corresponding object that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud. The operations also comprise assigning a plurality of weights that comprises assigning a respective weight to each respective cluster based on the respective geometric feature that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud and aligning the first point cloud with the second point cloud based on the plurality of weights.