The present disclosure provides a method and device for identifying a flashing light source. The method includes as follows. A processor obtains an image captured via a rolling shutter image sensor, the rolling shutter image sensor capturing an image of an environmental background, wherein the environmental background may include movable flashing light sources. A processor obtains stripe sets in the image, wherein the stripe sets can be obtained by capturing the flashing light sources via the rolling shutter image sensor. A processor takes a center of each stripe set as a reference point, and samples along a first direction to obtain n sampling points. A processor identifies the flashing light source by processing the n sampling points. With this disclosure, the identifying automatically of the flashing light source can be achieved.

Rotational speed of a rotating component is determined using frequency domain vibrational data. A time sequence of vibrational data of the rotating component is sensed and converted to the frequency domain vibrational data. A portion of the frequency domain vibrational data corresponding to an expected rotational speed of the rotating component is identified. A frequency bin index of the frequency domain vibrational data corresponding to a maximum vibration within the portion of the frequency domain vibrational data is identified. The maximum vibration at the identified frequency bin index and vibrations associated with adjacent frequency bin indices are fitted to a model curve. A floating point frequency bin index corresponding to a maximum of the model curve is identified, and the rotational speed of the rotating component is determined based on the frequency bin index corresponding to the maximum of the model curve.

Data analysis for structural health monitoring relating to a method of modal identification for structures with non-proportional damping based on extended sparse component analysis. Hilbert transform constructs analytical signal of acceleration response. Analytical signal is transformed into time-frequency domain using short-time-Fourier transform. The criterion is taken as the correlation coefficient of adjacent frequency points is close to 1. Points contributed by only one mode are detected from the time-frequency plane. Phases calculated at single-source-points are used to remove local outliers through local outlier factor method. Amplitudes of complex-valued mode shapes are estimated by Hierarchical clustering of amplitudes for time-frequency coefficients at single-source-points. Averaged phases of grouped single-source-points are estimated phases of complex-valued mode shapes. Finally, complex-valued mode shapes are acquired. Modal responses are estimated by sparse reconstruction method. This method extends application range of sparse component analysis method, and can identify complex modes of non-proportionally damped structures.

A method of converting a HOBO problem into a QUBO problem. The method may include creating a data structure of key-value pairs by sorting the plurality of indices of the variables of the HOBO problem, the key in each key-value pair corresponding to combinations of quadratic terms appearing in the HOBO and the value corresponding to all terms of at least degree three that contain the associated key. For each key of the data structure, a quadratization process is performed including identifying a key with the largest number of associated values, replacing the identified key with an auxiliary variable, and updating the data structure so as to correspond with the replacement of the auxiliary variable, storing the auxiliary variable and a quadratic term the auxiliary variable replaced as a pair in a data map. The method may also include constructing a quadratic polynomial for each pair in the data map.

A brake actuator for a rail vehicle has a sensor for detecting a braking force generated in the brake actuator, a sensor for detecting vibrations generated by the braking force in the brake actuator, and a control unit. The control unit is configured so as to generate a relationship between the speed of the rail vehicle with a generated braking force and a frequency of the vibrations generated by the braking force.

Methods and systems for analyzing an industrial structure are provided. With a plurality of sensors (e.g. FBGs and/or piezoelectric transducers and/or electromagnetic acoustic transducers) deployed in, on or in proximity to the structure, sensors are interrogated and a function representative of the impulse response of the structure is determined by passive inverse filter. Subsequently, a map of the propagation of the elastic waves through the structure is determined via various modalities, and in particular by tomography (of bulk or guided waves, by analysis of time of flight or of the complete signal). Embodiments especially relate to the management of the number and position of the sensors, to the use of artificial noise sources, and to automatically controlling the sensors and/or noise sources to monitor the health of the structure, or even to view the dynamic behavior of the structure.

A system performs convolution operations based on an analysis of the input size. The input includes data elements and filter weights. The system includes multiple processing elements. Each processing element includes multipliers and adders, with more of the adders than the multipliers. According to at least the analysis result which indicates whether the input size matches a predetermined size, the system is operative to select a first mode or a second mode. In the first mode, a greater number of the adders than the multipliers are enabled for each processing element to multiply transformed input and to perform an inverse transformation. In the second mode, an equal number of the adders and the multipliers are enabled for each processing element to multiply-and-accumulate the input. One or more of the multipliers are shared by the first mode and the second mode.

A processor calculates, from image data, a plurality of first coefficients corresponding to different frequencies using a Fourier transform and stores first frequency domain data, from which first coefficients whose magnitude is below a threshold have been excluded, in a memory. The processor also calculates, from kernel data, second coefficients of frequencies corresponding to the first coefficients indicated by the first frequency domain data using a Fourier transform and stores second frequency domain data in the memory. The processor uses the first frequency domain data, the second frequency domain data, and an inverse Fourier transform to generate a feature map indicating a result of applying the kernel data to the image data.

Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.

The present disclosure provides a method and device for identifying a flashing light source. The method includes as follows. A processor obtains an image captured via a rolling shutter image sensor, the rolling shutter image sensor capturing an image of an environmental background, wherein the environmental background may include movable flashing light sources. A processor obtains stripe sets in the image, wherein the stripe sets can be obtained by capturing the flashing light sources via the rolling shutter image sensor. A processor takes a center of each stripe set as a reference point, and samples along a first direction to obtain n sampling points. A processor identifies the flashing light source by processing the n sampling points. With this disclosure, the identifying automatically of the flashing light source can be achieved.