Abstract: A sensor arrangement comprises a communication device and a sensor element. The sensor element is configured to record a property and provide a sensor signal that represents the property. The sensor arrangement comprises a security element configured to provide a secret. The sensor arrangement is configured to link the sensor signal to the secret to obtain a linked sensor signal, transmit the linked sensor signal to a communication partner using the communication device, obtain a test signal from the communication partner using the communication device, and perform a check to determine whether the test signal comprises the secret.

Abstract: An abnormality diagnosis method for diagnosing an abnormality in operational state of a diagnosis subject includes creating a unit space from normal operation data of the diagnosis subject, the unit space serving as a reference for determining the operational state of the diagnosis subject, acquiring data having state quantities of a plurality of evaluation items from the diagnosis subject, calculating a Mahalanobis distance of the data acquired, using the unit space created, and determining an abnormality in the operational state of the diagnosis subject based on the Mahalanobis distance calculated. The creating a unit space includes creating a plurality of unit spaces having mutually different data lengths The calculating a Mahalanobis distance includes calculating a plurality of Mahalanobis distances using the plurality of unit spaces created. The determining an abnormality includes determining an abnormality based on the plurality of Mahalanobis distances calculated.

Abstract: A method of determining the draft of a vessel comprising the steps of: measuring the draft of the vessel using at least one optical imaging device to provide optical draft measurement data; measuring the draft of the vessel using elevation data provided by at least one GNSS or GPS device so as to provide elevation draft measurement data; and using the elevation draft measurement data and the optical draft measurement data to determine the draft of the vessel.

Abstract: Embodiments of the present disclosure provide an operation and maintenance system and method. The operation and maintenance system comprises a plurality of interconnected modules including: a data acquisition module, a data storage module, an exception and fault labeling module, an automatic model training and assessment module, an operation and maintenance management and task execution module, and a result checking module.

Abstract: A deviation detection method includes obtaining first and second images of first and second electrode sheets, respectively, of an electrode assembly during winding using first and second cameras. The method further includes determining whether the first and/or second camera is deviated according to a distance from a reference point in the first and/or second image to a specific position, which is a position of a same object appearing in the first image and the second image. The method also includes determining whether the electrode assembly is deviated according to a vertical distance from the reference point in the first image to a boundary of the first electrode sheet and a vertical distance from the reference point in the second image to a boundary of the second electrode sheet if the first camera and the second camera are not deviated.

Abstract: An apparatus, system, method, and a control program on a recording medium are provided, each of which: acquires first point cloud data, and second point cloud data different from the first point cloud data; sets a plurality of search start positions each used for alignment form the first point cloud data to the second point cloud data; searches, for each of one or more of the plurality of search start positions, a solution candidate for coordinate transformation from the first point cloud data to the second point cloud data, to generate a plurality of solution candidates; and determines a final solution, from the plurality of solution candidates.

Abstract: A system and a method for detecting a moving target based on multi-frame point clouds. The system comprises a voxel feature extraction module; a transformer module used for matching and fusing the feature tensor sequence, fusing a first feature tensor with a second feature tensor, fusing the fused result with a third feature tensor, fusing the fused result with a fourth feature tensor, and repeating the fusing steps with a next feature tensor to obtain a final fused feature tensor; and an identification module used for extracting features from the final fused feature tensor and outputting detection information of a target. The method comprises the following steps: S1, constructing each system module; S2, training the model by the data in a training set; S3, predicting by the trained model.

Abstract: Spatial data may be divided along an axis of the second dimension into a first data segment and a second data segment, such that the first data segment is limited to data points of the spatial data with second dimension coordinates within a first range and the second data segment is limited to data points of the spatial data with second dimension coordinates within a second range. A first processing element may execute an object detection process on the first data segment to generate a first list of objects within the first data segment. A second processing element may execute the object detection process on the second data segment to generate a second list of objects within the second data segment. A first set of objects detected in the first data segment may be combined with a second set of objects detected in the second data segment.

Abstract: Techniques and apparatus for performing a power-on self-test for a braking system in an autonomous delivery robot are described. One technique includes moving each wheel of the autonomous delivery robot in a first direction in accordance with predefined criteria, while brake module(s) of the braking system are engaged to one or more of the wheels to stop movement of the one or more wheels. A first amount of movement of at least a first wheel that is engaged to a first brake module is determined. Upon determining that the first amount of movement satisfies a predetermined condition, a determination is made that the braking system has failed the test and the brake module(s) of the braking system are kept in an engaged state.

Abstract: Provided are a basic data input unit to input basic data including molding conditions data related to molding conditions and screw data related to the form of the screw; a calculation formula data setting unit to set solid phase rate calculation formula data to calculate the solid phase rate of the molten resin in a heating cylinder based on this basic data; a calculation processing function unit having a solid phase rate calculation processing unit to use calculation processing based on the basic data and the solid phase rate calculation formula data to calculate an estimated solid phase rate of the molten resin at the measurement completion; and an output processing function unit that performs display processing to display information related to the estimated solid phase rate on a display.

Abstract: A modified version of a MEMS self-test procedure is presented that can be used to detect the amplitude and frequency of an external vibration from an ambient environment. The method implements processing circuitry that correlates an output sense signal, s(t), with a plurality of periodic signal portions and a plurality of shifted periodic signal portions to generate a plurality of correlation values. A frequency associated with the external vibration is determined based on the plurality of correlation values.

Abstract: A method of servicing equipment, the method comprising: recording information associated with servicing of the equipment in view of a workscope at a first location; sending the recorded information to a node; receiving service input from a third party located at a second location different from the first location, the third party having prepared the service input in response to the recorded information on the node; and performing the service using the received service input.

Abstract: In an approach to digital twin enabled equipment diagnostics based on acoustic modeling, a real-time audio input of an asset is received from a mobile device. The real-time audio input is analyzed using one or more acoustic modeling algorithms to establish a deviation from a baseline, where the baseline is associated with a digital twin of the asset. Responsive to determining the deviation from the baseline exceeds a predetermined threshold, the user is iteratively directed to move the mobile device until a stopping criteria is met.

Abstract: The present invention relates to a system for monitoring food waste. The system includes a weight mechanism configured for weighing a waste receptacle, wherein the waste receptacle is configured for receiving food waste from a plurality of consecutive disposal events before emptying, a processor configured for measuring the difference in weight of the waste receptacle between each disposal event and calculating the weight of a disposal event based upon the difference and a user interface configured to receive at least one indication categorising the food waste in a disposal event by a user. A method for monitoring food waste is also described.

Abstract: A wearable computing device, including a device body, an inertial measurement unit (IMU), and a processor. The processor may receive, from the IMU, a plurality of kinematic measurements collected within a time window. With one or more machine learning models, based at least in part on the kinematic measurements, the processor may compute a current velocity estimate for the wearable computing device at a current timestep and a prior velocity estimate for the wearable computing device at a prior timestep. The processor may compute a current pose estimate and a prior pose estimate based at least in part on the current velocity estimate and the prior velocity estimate, respectively. The processor may compute a composite pose estimate for the wearable computing device at the current timestep based on the current pose estimate and the prior pose estimate. The processor may output the composite pose estimate to a target program.

Abstract: In a flow rate sensor 10, a resistance bridge 100 detects a change in a gas flow rate. A differential A/D converter 122 converts an analog signal output from the resistance bridge 100 into a digital signal. A disconnection detection circuit 130 detects disconnection of bonding wires 164, 165 that connect the differential A/D converter 122 and the resistance bridge 100. The disconnection detection unit 130 includes a detection capacitance 141 and a comparator 145. One connection portion of the detection capacitance 141 is connected to an input node of the A/D converter 122. The comparator 145 detects the disconnection of the bonding wires 164, 165 based on a potential based on a ratio of an electrostatic capacitance of a P-side input capacitance 116 or a N-side input capacitance 117 to an electrostatic capacitance of the detection capacitance 141. The P-side input capacitance 116 and the N-side input capacitance 117 respectively are connected to operation input units of the differential A/D converter 122.

Abstract: A system for asset identification and mapping is configured to information related to a plurality of objects with at least one sensor. The captured information may be processed to identify one or more assets among the objects, and a map generated including a geographic area proximate to at least one of the one or more assets. The at least one of the one or more assets may then be identified on the map.

Abstract: Proposed is a method of measuring the slope of a drainpipe while moving through the drainpipe. The method includes following steps: a) continuously measuring a slope of a pipe using a slope sensor disposed in a vehicle when the vehicle moves; b) measuring distances to a ceiling of the pipe in real time through non-contact sensors disposed at four positions, that is, at both sides of front and rear portions the vehicle, the step b) being performed simultaneously with the step a); and c) calculating slope differences, which are the degrees of inclination to the front, rear, left, and right using trigonometry and then correcting the slope by reflecting the slope differences to the slope measured in the step a), when there are differences in the distances measured in step b).

Abstract: Apparatus and associated methods relate to a self-contained ocean data and acquisition module (SCODAM) configured to mount to a floating body and having a sensor array, geospatial locating engine, wave measurement engine, communication engine to transmit collected data to a remote device, an energy conversion module adapted to convert ambient energy inputs into electrical energy, and an energy storage module configured to receive the converted electrical energy and to supply operating power to the SCODAM. In an illustrative example, the SCODAM may be configured to generate a transfer function based on motion characterization data obtained in a training mode corresponding to motion of the floating body in response to perturbation in a predetermined sequence and to apply the transfer function data obtained by the wave measurement engine to determine wave motion. Various embodiments may advantageously facilitate use of an existing floating body as an ocean data acquisition system (ODAS).

Abstract: A method for inertial sensor error modeling and compensation comprises obtaining multiple bias drift datasets for an elapsed time period for one or more gyroscopes; generating a 3D bias drift data plot using the multiple bias drift datasets; generating a partial bias drift data image based on the 3D bias drift data plot; and inputting the partial bias drift data image into a machine learning algorithm to predict how bias drift evolves over time for the gyroscopes. The machine learning algorithm uses the partial bias drift data image, the elapsed time period, and temperature history to compute a predicted bias over temperature with respect to time, to thereby predict bias drift data for a future time period for the gyroscopes. The machine learning algorithm outputs a completed bias drift image that represents drift data from the elapsed time period and the predicted bias drift data for the future time period.