Abstract: A data processing method includes: generating a service serial number for a target service according to a preset naming rule; obtaining service data of the target service; obtaining a target data table from a plurality of pre-configured data tables, according to the service serial number; and storing the service data to the target data table.

Abstract: A modular wave energy converter includes: a forward attachment frame; forward guide rails coupled to the forward attachment frame; a forward paddle coupled to the forward guide rails; and one or more forward tethers coupled to the forward paddle and the shaft. A combination of heave and surge forces from waves of water causes the forward paddle moves up and down the forward guide rails. The movement of the forward paddle moves the one or more forward tethers. The movement of the one or more forward tethers causes the shaft to rotate via the rotation of winches. The winches are configured with a one-way clutch, which allows the shaft to rotate in a first direction but not a second. The converter has the same structure on the aft side, including an aft paddle. The forward and aft paddles are positioned vertically or inclined.

Abstract: A modular wave energy converter includes: a forward attachment frame; forward guide rails coupled to the forward attachment frame; a forward paddle coupled to the forward guide rails; and one or more forward tethers coupled to the forward paddle and the shaft. A combination of heave and surge forces from waves of water causes the forward paddle moves up and down the forward guide rails. The movement of the forward paddle moves the one or more forward tethers. The movement of the one or more forward tethers causes the shaft to rotate via the rotation of winches. The winches are configured with a one-way clutch, which allows the shaft to rotate in a first direction but not a second. The converter has the same structure on the aft side, including an aft paddle. The forward and aft paddles are positioned vertically or inclined.

Abstract: A modular wave energy converter includes: a forward attachment frame; forward guide rails coupled to the forward attachment frame; a forward paddle coupled to the forward guide rails; and one or more forward tethers coupled to the forward paddle and the shaft. A combination of heave and surge forces from waves of water causes the forward paddle moves up and down the forward guide rails. The movement of the forward paddle moves the one or more forward tethers. The movement of the one or more forward tethers causes the shaft to rotate via the rotation of winches. The winches are configured with a one-way clutch, which allows the shaft to rotate in a first direction but not a second. The converter has the same structure on the aft side, including an aft paddle. The forward and aft paddles are positioned vertically or inclined.

Abstract: Various examples of simultaneous ocean wave and current energy harvesting are described, using a hybrid ocean energy converter and a mechanical transfer system therefor. In one example, a hybrid ocean energy converter includes a two-body point absorber comprising a first body and a second body. The two-body point absorber can be configured to transfer a linear relative motion between the first body and the second body to bi-directional rotation of a first input shaft. A turbine can be configured rotate a second input shaft. The converter further includes a hybrid power takeoff including a mechanical transfer system configured to mechanically couple the first input shaft, the second input shaft, an output shaft, and an output shaft.

Abstract: A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

Abstract: A kinetic energy harvesting device implementing a half-wave mechanical motion rectification (HMMR) mechanism is described. The energy harvesting device may include an energy harvesting railroad tie, where the tie includes a tie housing configured to be coupled to at least one rail. The tie housing includes at least one spring and at least one energy harvester. The at least one energy harvester includes a generator having an output shaft, a gearhead, a ball screw or other motion transmission, and a one-way clutch. Under a force of a passing wheel on the at least one rail, the at least one energy harvester is compressed, which causes a rotation of the ball screw or other motion transmission, a rotation of the output shaft, and a transmission of torque to a shaft of the gearhead, driving the generator in a unidirectional rotation to generate electrical power via the one-way clutch.

Abstract: Aspects of triboelectric fibers and methods of manufacture of the fibers are described. In one example, a method of manufacture of a fiber for generating energy using the triboelectric effect includes forming a preform tube, heating the preform tube in a furnace, feeding a wire through the preform tube and the furnace during the heating, and pulling the wire through the furnace to form a fiber. The methods described herein can be relied upon to manufacture fibers long enough for industrial-scale textile manufacturing, including for use with industrial-scale looms. In one example, forming the preform tube can include providing a polypropylene tube and wrapping the polypropylene tube with a housing layer of amorphous film, such as acrylic film. The acrylic film can be relied upon to maintain the form and integrity of the polypropylene as the wire is pulled, and the acrylic film can be easily removed after the pulling.

Abstract: A method for diagnosing analog circuit fault based on cross wavelet features includes steps of: inputting an excitation signal to an analog circuit under test, and collecting time domain response output signals to form an original data sample set; dividing the original data sample set into a training sample set and a test sample set; performing cross wavelet decomposition on both sets; applying bidirectional two-dimensional linear discriminant analysis to process the wavelet cross spectra of the training sample set and the test sample set, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set into the model to perform fault classification.

Abstract: Disclosed are a vehicle anti-collision forewarning method, an in-vehicle computer device and a vehicle anti-collision forewarning system.

Abstract: Provided are methods and compositions for the prevention and treatment of diabetes complications. The methods include administration of pharmaceutical compounds containing a novel chrysin derivative compound as an active ingredient. More specifically, the pharmaceutical compositions inhibit the formation of an advanced glycation end product (AGE) thereby preventing or treating diabetes complications.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: Disclosed are a vehicle anti-collision forewarning method, an in-vehicle computer device and a vehicle anti-collision forewarning system.

Abstract: Disclosed is a pharmaceutical composition for preventing or treating diabetes complications comprising a novel chrysin derivative compound as an active ingredient and, more specifically, a pharmaceutical composition for preventing or treating diabetes complications containing, as an active ingredient, a novel chrysin derivative compound that is capable of preventing or treating diabetes complications due to the ability thereof to inhibit the formation of an advanced glycation end product (AGE).

Abstract: A method for diagnosing analog circuit fault based on cross wavelet features includes steps of: inputting an excitation signal to an analog circuit under test, and collecting time domain response output signals to form an original data sample set; dividing the original data sample set into a training sample set and a test sample set; performing cross wavelet decomposition on both sets; applying bidirectional two-dimensional linear discriminant analysis to process the wavelet cross spectra of the training sample set and the test sample set, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set into the model to perform fault classification.

Abstract: An electromagnetic interference objective complexity evaluation method based on a fast S-transformation time-frequency space model comprises the following steps: (1) carrying out fast S-transformation on space electromagnetic signals acquired in real time to obtain a fast speed S-transformation two-dimensional time-frequency matrix of the signal; (2) calculating time domain occupancy rate TP, frequency occupancy rate FP, and energy occupancy rate EP of an evaluation object and interference signals in the fast speed S-transformation two-dimensional time-frequency matrix; (3) calculating electromagnetic interference objective complexity C=TP*FP*EP according to the time-frequency space model; and (4) finding a grading standard according to an objective complexity value so as to determine an electromagnetic interference objective complexity type.

Abstract: A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

Abstract: An electromagnetic interference objective complexity evaluation method based on a fast S-transformation time-frequency space model comprises the following steps: (1) carrying out fast S-transformation on space electromagnetic signals acquired in real time to obtain a fast speed S-transformation two-dimensional time-frequency matrix of the signal; (2) calculating time domain occupancy rate TP, frequency occupancy rate FP, and energy occupancy rate EP of an evaluation object and interference signals in the fast speed S-transformation two-dimensional time-frequency matrix; (3) calculating electromagnetic interference objective complexity C=TP*FP*EP according to the time-frequency space model; and (4) finding a grading standard according to an objective complexity value so as to determine an electromagnetic interference objective complexity type.

Abstract: A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.