Abstract: The embodiments of the present disclosure provide a method for predicting a gate station filter replacement at a gate station for smart gas and an Internet of Things system. The method includes: obtaining, by a smart gas data center, usage information of a filter element through a smart gas sensor network platform, the usage information at least including at least one of ventilation efficiency of the filter element, filtered impurity information, and a blockage degree; obtaining, by a smart gas pipeline network device management sub-platform, the usage information from the smart gas data center, determining a filter element maintenance plan based on the usage information, and sending the filter element maintenance plan to the smart gas data center; and sending, by the smart gas data center, the filter element maintenance plan to a smart gas user platform through a smart gas service platform.

Abstract: A three-dimensional data creation method for use in a vehicle including a sensor and a data receiver that transmits and receives three-dimensional data to and from an external device. The three-dimensional data creation method includes: creating second three-dimensional data based on information detected by the sensor and first three-dimensional data received by the data receiver; and transmitting, to the external device, third three-dimensional data that is part of the second three-dimensional data.

Abstract: A flexural wave absorption system detects, with a sensor attached to a beam, an incident wave propagating in the beam. The system determines, based on a signal from the sensor generated in response to the incident wave, an amplitude and phase of the incident wave propagating in the beam and controls an actuator connected to the beam to generate a suppression wave, based on the amplitude and the phase of the incident wave, that reduces a coefficient of reflection of the incident wave across a broadband frequency range.

Abstract: A method includes obtaining a plurality of master sensor responses with a master sensor in a set of training fluids and obtaining node sensor responses in the set of training fluids. A linear correlation between a compensated master data set and a node data set is then found for a set of training fluids and generating node sensor responses in a tool parameter space from the compensated master data set on a set of application fluids. A reverse transformation is obtained based on the node sensor responses in a complete set of calibration fluids. The reverse transformation converts each node sensor response from a tool parameter space to the synthetic parameter space and uses transformed data as inputs of various fluid predictive models to obtain fluid characteristics. The method includes modifying operation parameters of a drilling or a well testing and sampling system according to the fluid characteristics.

Abstract: A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Abstract: The embodiments of the present disclosure provide a method and an Internet of Things (IoT) system for gas purification management in a storage and distribution station for smart gas. The method is implemented based on the IoT system for gas purification management in the storage and distribution station for smart gas. The IoT system includes a smart gas user platform, a smart gas service platform, a smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform. The method is executed by the smart gas device management platform. The method includes: obtaining a gas quality condition parameter of a raw gas, and the gas quality condition parameter being obtained based on the storage and distribution station; determining, based on the gas quality condition parameter, an operation parameter in a purification process, the operation parameter being used for a purification operation of the raw gas.

Abstract: Methods and systems for inspecting the integrity of multiple nested tubulars are provided. A method for inspecting the integrity of multiple nested tubulars can comprise conveying an electromagnetic pipe inspection tool inside the innermost tubular of the multiple nested tubulars; taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool; inverting the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties; arranging the inverted integrity properties into an inverted image representative of an estimated tubular integrity property of each individual tubular; and feeding the inverted image to a pre-trained deep neural network (DNN) to produce a corrected image, wherein the DNN comprises at least one convolutional layer, and wherein the corrected image comprises a representation of the tubular integrity property of each individual tubular of the multiple nested tubulars.

Abstract: From each of a plurality of cameras, a visual input of a location is received over a network. For each visual input from the plurality of cameras, a coupling correction is performed between a shaking of the camera with respect to the visual input by subtracting velocity vectors of the plurality of cameras from velocity vectors of pixels defining the visual input to provide a processed input. It is determined whether a shaking identified in the processed input is above a predetermined threshold based on the processed input, thereby detecting one or more anomalies. From the one or more anomalies, at least one of a location, magnitude, or depth of an earthquake are inferred based on the shaking identified in the processed input of each of the plurality of cameras.

Abstract: A method including: acquiring present output current values of photovoltaic strings included in a photovoltaic array that, wherein includes at least two photovoltaic strings in parallel; determining a target abnormality score predicting model based on the present output current values. Determining a present abnormality score corresponding to the photovoltaic array by inputting the present output current values into the target abnormality score predicting model; and determining the operating state of the photovoltaic array by comparing the present abnormality score with a target abnormality score. The present abnormality score is output by inputting the present output current values acquired in real-time into the abnormality score predicting model, so as to determine the operating state of the photovoltaic array.

Abstract: A quality check module for characterizing a specimen and/or a specimen container. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more cameras located at one or more viewpoints adjacent to the imaging location, and one or more spectrally-switchable light source including a light panel assembly located adjacent the imaging location and configured to provide lighting for the one or more cameras, the spectrally-switchable light source configured to be operatively switchable between multiple different spectra. Methods of imaging a specimen and/or specimen container and specimen, and specimen testing apparatus including a quality check module adapted to carry out the method are described herein, as are other aspects.

Abstract: A method is disclosed for determining mechanical robustness of an overhead stowage bin for an aircraft includes repeatedly effecting an impact of a test body against an impact surface of the overhead stowage bin with a predefined impact force by a robotic arm of a manipulator, and investigating damage parameters of the overhead stowage bin.

Abstract: A method includes receiving a plurality of sets of sensor data associated with a processing chamber of a substrate processing system. Each of the plurality of sets of sensor data comprises a corresponding sensor value of the processing chamber mapped to a corresponding spacing value of the processing chamber. The method further includes providing the plurality of sets of sensor data as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of a health of the processing chamber. The method further includes causing, based on the one or more outputs, performance of one or more corrective actions associated with the processing chamber.

Abstract: Devices, systems, and methods are provided for enhanced sensor alignment. A device may determine a first array of displacement sensors proximate to a first test structure. The device may determine a second array of displacement sensors proximate to a second test structure. The device may apply a test condition to the first array, the second array, the first test structure, and the second test structure. The device may collect a first output from applying the test condition to the first test structure. The device may collect a second output from applying the test condition to the second test structure. The device may generate a first deviation vector associated with the first output. The device may generate a second deviation vector associated with the second output. The device may determine a first design status of the first structure based on the first deviation vector. The device may determine a second design status of the second structure based on the second deviation vector.

Abstract: A computer-vision-based fatigue crack detection approach using a short video is described. Feature tracking is applied to the video for tracking the surface motion of the monitored structure under repetitive load. Then, a crack detection and localization algorithm is established to search for differential features at different frames in the video. The effectiveness of the proposed approach is validated through testing two experimental specimens with in-plane and out-of-plane fatigue cracks. Results indicate that the proposed approach can robustly identify fatigue cracks, even when the cracks are under ambient lighting conditions, surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes due to crack closure. The approach enables accurate quantification of crack openings under fatigue loading with good accuracy.

Abstract: A measurement method includes: a step of acquiring first observation point information including a time point when each part of an m-th moving object passes a first observation point and a physical quantity which is a response to an action; a step of acquiring second observation point information including a time point when the each part passes a second observation point and a physical quantity which is a response to an action; a step of calculating a deflection waveform of a structure generated by the each part; a step of adding the deflection waveforms to calculate an m-th moving object deflection waveform; a step of calculating a displacement waveform at the third observation point; and a step of calculating first to M-th amplitude coefficients by assuming that a waveform obtained by multiplying an m-th amplitude coefficient by the m-th moving object deflection waveform is an m-th amplitude adjusted deflection waveform, and that a sum of first to M-th amplitude adjusted deflection waveforms is approximated

Abstract: A method of determining and controlling a weight flow in an environmental control system includes sensing, using a turbine inlet temperature sensor, a turbine inlet temperature. A turbine inlet pressure is sensed using a turbine inlet pressure sensor. A turbine outlet pressure is sensed using a turbine outlet pressure sensor. A rotational shaft speed of a shaft is sensed using a rotational shaft speed sensor. The sensed turbine inlet temperature, the sensed turbine inlet pressure, the sensed turbine outlet pressure, and the sensed rotational shaft speed are received by a controller. A flow coefficient is determined by the controller using the turbine inlet pressure, the turbine outlet pressure, the shaft speed, and a Turbine Flow Coefficient Map. A weight flow through the turbine is determined by the controller using the flow coefficient, the turbine inlet temperature, a nozzle area, and the turbine inlet pressure.

Abstract: Methods and systems for relative inertial measurement may include a user device comprising an inertial measurement device and/or a camera. A second inertial measurement device may be configured to move with a reference frame. One or more processors may receive inertial measurements from the first and second inertial measurement devices and determine movement of the user device relative to the reference frame by comparing the received inertial measurements. Additionally reference objects in a view of a camera may be used to calibrate the determined motion of the user device within the reference frame.

Abstract: A set of load responses of an asset for a sample of traffic loading events caused by objects of unknown weight is measured. At least one statistical parameter is determined from the set of load responses. A corresponding statistical parameter of known object weights loading the asset is determined. An object weight is assigned to a load response of the set of load responses based on correlation of the extracted statistical parameter to the corresponding statistical parameter.

Abstract: An analysis data processing method for processing analysis data collected with an analyzing device for each of a plurality of samples, by applying an analytical technique using statistical machine learning to multidimensional analysis data formed by output values obtained from a plurality of channels of a multichannel detector provided in the analyzing device, the method including: acquiring a non-linear regression or non-linear discrimination function expressing analysis data obtained for known samples; calculating a contribution value of each of the output values obtained from the plurality of channels forming the analysis data of the known samples, to the acquired non-linear regression or non-linear discrimination function, based on a differential value of the non-linear regression function or non-linear discrimination function; and identifying one or more of the plurality of channels of the detector, which are to be used for processing analysis data obtained for an unknown sample, based on the contributio

Abstract: A movable system for automatically monitoring the correlated wind and temperature filed of a bridge, including a bridge monitoring subsystem, a cloud server, and a client. The system monitors the meteorological parameters of a bridge surface and a temperature of a bridge structure, performs data analysis and processing on a cloud server, and performs visual data interaction by using a client. A bridge surface-specific meteorological parameter monitoring module is movable, such that the location of the sensor for meteorological data monitoring can be adjusted at any time to monitor an entire bridge deck in a time-sharing manner. A lower cantilever structure has an adjustable height, such that the sensor for meteorological data monitoring can track a height of a boundary layer of the bridge surface. A bridge structure-specific temperature monitoring module adopts distributed patch-type temperature sensors, which can detect the temperature of the bridge structure in all directions.