Abstract: Disclosed is a method for qualitative and quantitative determination of key substances in mixture based on terahertz spectrum. Terahertz spectrum of a reference mixture is trained through an SVR algorithm, and predicting parameters of key substances in the mixture to be determined after a model is generated. According to the method, an initial pure spectrum corresponding to each composition in the mixture does not need to be separately determined, no limitation on the number of samples contained in the mixture, and no limitation on frequency range to be determined, and the proportion requirement of the mixture in the early test stage is not limited, and the SVR model does not need to be re-trained after a database is formed in the later stage, and the result can be obtained immediately after the spectrum of item to be determined is introduced into the algorithm model.

Abstract: A computing system receives a request for computer-generated likelihood(s) for candidate evaluations of an industrial product set according to a measurement system analysis (MSA). The MSA comprises tests for evaluating, according to a measurement standard, the industrial product set. The request indicates a metric set representing metric(s) each quantifying an estimate of contribution to variation in evaluating the industrial product set according to the MSA. The system generates a design comprising a respective input set for each test of the MSA. The respective input set comprises setting(s) for conducting a test of the MSA. The design is designed to isolate candidate sources for contributing to the variation in evaluating the industrial product set according to the MSA. The system (e.g., prior to the MSA) outputs, based on the metric set and the design, the computer-generated likelihood(s) for the candidate evaluations of the industrial product set according to the MSA.

Abstract: A precipitation index estimation apparatus includes a data collection unit and an estimation processing unit. The data collection unit is configured to collect rain-related data for estimating a precipitation index indicating an intensity of precipitation within a predetermined period from vehicle state information generated in one or more vehicles. The estimation processing unit is configured to carry out a statistical processing of the collected rain-related data and estimate the precipitation index within the predetermined period. The estimation processing unit is configured to correct, before the statistical processing, a value of the rain-related data to be used for the statistical processing, according to speed of a vehicle at the time at which the rain-related data is acquired.

Abstract: A battery diagnostic system is disclosed for an RUL estimation of a battery. The battery diagnostic system comprises a memory configured to store a neural network trained to estimate a remaining useful life (RUL) of a test battery from a predetermined set of features indicative of a battery cycle of the test battery and a capacity of the test battery; a charging system configured to charge and discharge the test battery to provide measurements of the battery cycle and the capacity of the test battery; a processor configured to extract the predetermined set of features from the measurements to submit the extracted set of features to the neural network and an output interface configured to output the estimated RUL of the test battery.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A method for health assessment of a system including a transport apparatus including registering predetermined operating data embodying at least one dynamic performance variable output by the transport apparatus, determining a base value (CpkBase) characterized by a probability density function of each of the dynamic performance variable output, resolving from the transport apparatus in situ process motion commands of the apparatus controller and defining another predetermined motion set of the transport apparatus, registering predetermined operating data embodying the at least one dynamic performance variable output by the transport apparatus and determining with the processor another value (CpkOther) characterized by the probability density function of each of the dynamic performance variable output by the transport apparatus, and comparing the other value and the base value (CpkBase) for each of the dynamic performance variable output by the transport apparatus respectively corresponding to the predetermin

Abstract: A system, apparatus, and/or method is disclosed for producing a personal care product. An identity of a considered chemical composition may be input into a model (e.g., a machine learning model). The identity of the considered chemical composition may include ingredients. Each of the ingredients of the considered chemical composition may be associated with a value of a chemoinformatic property of chemoinformatic properties of the considered chemical composition. A value of the property of the considered chemical composition may be determined via the model. The value may be based on the identity of the considered chemical composition. The property of the considered chemical composition may be affected by an interaction of at least two of the ingredients of the considered chemical composition. A personal care product comprised of the considered chemical composition may be produced.

Abstract: A device may include a processor that may recover the signals misallocated in the deblending process of seismic data acquired with simultaneous sources. The processor may update the primary signal estimate based at least in part on a separation operation that separates coherence signals from noise signals in an output associated with the residual determined to be remaining energy for separation. The processor may be incorporated into the iterative primary signal estimate of the deblending process or be applied towards preexisting deblending output. In response to satisfying an end condition, the processor may transmit a deblended output that includes the weak coherence signals recovered from the misallocation or error in the primary signal estimate. The processor may also transmit the deblended output for use in generating a seismic image. The seismic image may represent hydrocarbons in a subsurface region of Earth or subsurface drilling hazards.

Abstract: A system, apparatus, and/or method is disclosed for producing a personal care product. An identity of a considered chemical composition may be input into a model (e.g., a machine learning model). The identity of the considered chemical composition may include ingredients. Each of the ingredients of the considered chemical composition may be associated with a value of a chemoinformatic property of chemoinformatic properties of the considered chemical composition. A value of the property of the considered chemical composition may be determined via the model. The value may be based on the identity of the considered chemical composition. The property of the considered chemical composition may be affected by an interaction of at least two of the ingredients of the considered chemical composition. A personal care product comprised of the considered chemical composition may be produced.

Abstract: An apparatus and method determine movement information for a user that carries an accelerometer whilst moving. The apparatus receives acceleration data from the accelerometer defined relative to a frame of reference of the accelerometer. A transformation is determined and applied to the acceleration data or to data derived from the acceleration data to determine acceleration data is a user frame of reference that includes a user direction of travel and a side to side direction transverse to the user direction of travel. The acceleration data or data derived from the acceleration data is analyzed to determine a time period corresponding either to a user stride period or to a step period as the user is walking or running; and information about accelerations in the side to side direction are used to disambiguate whether the determined time period corresponds to the user stride period or to the user step period.

Abstract: Systems, methods, and computer readable media for power outage determination. A device may determine an indication that an outage has occurred on a cable network based on a status of a network device within the cable network, wherein the network device receives power from a power network. The device may determine location information for at least a portion of a plurality of network devices, including the network device, within the cable network. The device may determine a cable topology of the cable network at an area that includes a location that corresponds to the network device. The device may determine a power topology of the power network at the area. The device may analyze the cable topology and the power topology at the area. The device may identify one or more potential failure locations on the power network.

Abstract: A system includes sensors for monitoring pressure, flow, pump speed, temperature, and/or other signals at the output of a main hydraulic pump, and a processing system executes one or more methods for identification of hydraulic system events, from the signals, corresponding to state changes and performance of the system and/or its subcomponents. Event identification is performed with classification and/or other machine learning algorithms, with generation of novel training data sets. The sensor(s) can also be used to determine power consumption information about the system and/or its subcomponents. The system processes event-associated outputs for execution of actions for improving system performance, along with other downstream applications.

Abstract: Watershed hydrology analysis and management process and system with a network of weather stations and artificial drainage systems with artificial and natural reservoir management through locks and pumping stations. It evaluates potential hydrologic risk in each area and analyses the possible consequences of future precipitations using simulations. To make the simulation, it calculates hydrographs for each sub-basin, streams and rivers in the basin. It simulates the behavior of the basin under different scenarios corresponding to different types of management of the operation of locks and/or pumps and compares its results in terms of loss of flooded area, economic loss in each area, loss for flooding of urban areas, etc. Optimization of the simulation through artificial intelligence (AI, meta-heuristic algorithms, neural networks, etc.) allows it to act as a search engine to find better solutions and the best configuration of resource management that allows minimizing the socio-economic impact on each basin.

Abstract: A system and method of predicting variations in a capacity of a battery, the system including an ADFM management device including an experimental data collector to collect at least one first piece of data about the capacity of the battery, an ADF optimizer to optimize a first calculation equation, and a virtual data generator to generate at least one second piece of data; and a server including a training unit to train an artificial intelligence model for outputting the relative capacity variation value by using the at least one first piece of data and the at least one second piece of data as training data, and a prediction unit to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model.

Abstract: A tester for evaluating pullout load capacity and bond quality of anchor bolts embedded in concrete includes a Schmidt hammer for measuring a rebound number and an ultrasonic pulse velocity tester for measuring the transit time of a pulse transmitted through concrete surrounding an anchor bolt. The rebound number and the transit time are combined and matched against a database record which identifies the pullout load capacity and the bond quality. The transit time is matched to thresholds of transit times associated with porosity, internal cracking, air voids, and water pockets located around the embedded anchor bolt. The Schmidt hammer is further modified by the incorporation of a digital level for measuring the vertical and horizontal angles of inclination of the plunger with the concrete surface, a guide tube for supporting the plunger, and by using a convex plunger tip for improved registration with anchor bolt head.

Abstract: A gearbox monitoring system can include a fan, compressor, combustor, and turbine in axial flow arrangement, with corresponding rotating components mounted to a shaft, and a gearbox assembly operably coupled to the shaft and connecting the turbine and the fan. The gearbox assembly can include a carrier, a sun gear, at least one planet gear, and a ring gear. An oil feed tube is coupled to the gearbox for lubricating at least one of the sun, ring or planet gears. One or more bearings rotationally supports at least one of the sun gear, ring gear, or planet gear relative to the carrier. In addition, at least one vibration sensor is part of the gearbox monitoring system.

Abstract: The present disclosure relates to a battery management apparatus and method, which sets a degraded SOC region based on a SOC-based voltage difference between a charging voltage and a discharging voltage according to a SOC of a battery and estimates a degree of degradation of the battery based on the voltage difference in the set degraded SOC region.

Abstract: According to an aspect of the present disclosure, a correlation data correlating resource usage with workload signatures is maintained, each workload signature representing a cluster of block signatures, each block signature characterizing the transaction instances initiated in a corresponding block duration. For the transactions received in a current block duration, if a current block signature is not contained in the correlation data and if the current transaction arrival rate (TAR) is greater than an expected TAR, a resource requirement for the current block signature is computed. Actions to manage capacity to handle transaction instances are triggered if the resource requirement is greater than the resource allocation in the current block duration. As an unknown current block signature and a higher TAR may be indicative of a workload surge, triggering suitable actions for such block signatures facilitates such surges to be handled by the software application.

Abstract: A data processing method includes a sampling step of obtaining time series data based on a measurement result of a physical quantity in a substrate processing apparatus, an evaluation value calculation step of obtaining an evaluation value of the time series data by comparing the time series data with reference data, and a sampling period control step of controlling a sampling period used in the sampling step for each time series data. In the sampling period control step, all sampling periods are controlled to a normal period in an initial state, and when the evaluation value of the time series data is abnormal, the sampling period used when obtaining the time series data is controlled to an abnormal period shorter than the normal period.

Abstract: The disclosure provides a calibration method, a system and a device of an on-wafer S parameter of a vector network analyzer. The method comprises the steps of: acquiring a first parameter of a first crosstalk calibration piece measured by the vector network analyzer; obtaining a main crosstalk error term based on the first parameter of the first crosstalk calibration piece and a calibration parameter of the first crosstalk calibration piece; acquiring a second parameter of a second crosstalk calibration piece measured by the vector network analyzer based on the main crosstalk error term; and obtaining a secondary crosstalk error term based on the second parameter of the second crosstalk calibration piece and a calibration parameter of the second crosstalk calibration piece, wherein the main crosstalk error term and the secondary crosstalk error term are used for calibrating the vector network analyzer.