Abstract: In one embodiment, a bathymetry system includes a base slab, multiple spacer assemblies, a decking layer, multiple computer numerical control routed blocks, a waterproof coating, and multiple floor joints. The spacer assemblies are coupled with a top surface of the base slab, the decking layer is disposed on top surfaces of the spacer assemblies, the blocks are disposed on a top surface of the decking layer, floor joints are disposed between blocks and the decking layer, and the waterproof coating is disposed on top surfaces of the blocks. The top surfaces of the routed blocks are shaped so that the blocks collectively provide a bathymetry profile, such as a bottom contour of a riverbed.

Abstract: A method for human-robot collaboration including: acquiring visual temporal data of a human partner to a robot; determining, using a generative module, predicted future visual temporal data in response to the visual temporal data, the visual temporal data including current visual temporal data and previous visual temporal data; and determining, using a discriminative module, a vector of probabilities indicating the likelihood that a future action of the human partner belongs to each class among a set of classes being considered in response to at least the future visual temporal data and the visual temporal data.

Abstract: A method for obtaining information on quality of combustion of a liquor in a chemical recovery boiler. The method comprises entering a value of a first input parameter to a computational model configured to determine, based on the value of the first input parameter, a value of the quality of combustion of the liquor in the chemical recovery boiler, and at a first time, running the computational model to obtain a first modelling result. The method comprises measuring, from the chemical recovery boiler a value indicative of carryover to obtain a measurement result; comparing the first modelling result with [a] the measurement result or [b] a result derived from the measurement result, to obtain a primary comparison result; and adjusting the value of the first input parameter based on the primary comparison result to obtain an adjusted value of the first input parameter.

Abstract: A simulation method, implemented by computer, simulates the optical power of a laminated glass of given shape liable to be obtained by laminating at least two glass sheets and at least one lamination interlayer, the laminated glass being liable to have an edge, a first main face, and a second main face. The method is suitable for determining a minimum value of optical quality index which a combination of glass sheets must satisfy so as to form a laminated glass of given shape with a given optical quality constraint.

Abstract: The present invention discloses a large-scale model testing system of a floating offshore wind power generation device, and a method for manufacturing the large-scale model testing system. The large-scale model testing system comprises a floating wind power generation device model, model response measurement systems and environmental parameter measurement systems. The floating wind power generation device model comprises a floating foundation and a tower, wherein a wind turbine is connected to the top of the tower. A plurality of anchoring devices is connected to the side surface of the floating foundation. Each model response measurement system comprises an IMU unit, a wind turbine monitoring unit and an anchoring tension measurement unit. Each environmental parameter measurement system comprises a buoy-type wave height meter, a wind speed and direction meter and a flow velocity and direction meter.

Abstract: Embodiments of the disclosure provide method for performing contraction on a tensor network. The method can include: receiving, by a system, a tensor network comprising a plurality of tensors and a plurality of edges among the plurality of tensors, wherein each edge is associated with a plurality of index elements; determining a contraction order of the tensor network; determining, among the plurality of edges, one or more edges for generating a plurality of sub-networks based on the tensor network; and distributing the plurality of sub-networks to a plurality of computing nodes of the system to perform, by the plurality of computing nodes, contraction on the plurality of sub-networks based on the contraction order.

Abstract: A system may measure a response associated with a sample to an excitation. The system may compute, using the measured response and the excitation as inputs to an inverse model or a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The predetermined predictive model was trained using training data for different excitation strengths, different measurement conditions, or both. The forward model may simulate response physics occurring within the sample to a given excitation, and the model parameters may include magnetic susceptibilities of the multiple voxels. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output.

Abstract: Computational techniques are disclosed for using methylation profiles to classify the medication condition of a person. Initial sequence data is obtained containing sequences of an initial set of nucleic acids from a biological sample of a person. The initial sequence data is filtered to generate filtered sequence data that describes sequences of a filtered subset of nucleic acids from the biological sample. A methylation profile is determined for the filtered subset of nucleic acids from the biological sample. The methylation profile can be processed to determine a likelihood that the person has the specified medical condition. The system outputs an indication of the likelihood that the person has the specified medical condition.

Abstract: The present document describes techniques associated with a textile-material model for vibroacoustic structural simulation. The techniques described herein provide a nontrivial methodology to test a textile and simplify its representation, which can enable prediction of acoustic performance (e.g., rub and buzz) of an electronic-speaker device having a textile mounted thereon. The textile is modeled as a textile-material model based on an elongation stiffness obtained from a time-temperature superposition curve of the textile, which is based on a dynamic mechanical analysis test of the textile in each of course and wale directions. The textile-material model is then applied to an assembly model of the electronic-speaker device to simulate a vibroacoustic response of the textile relative to the assembly model to predict a likelihood of rub and buzz.

Abstract: Methods, machine readable media and systems for a method to determine if a model of an integrated circuit (IC) includes a Trojan component are described. In one embodiment, a method can include the following operations: splitting the model of the IC into a plurality of tiles; simulating the IC with an elevated temperature of each tile to predetermined level; computing a temperature-dependent leakage power for each tile; and identifying a tile of the IC as including a Trojan component based on the temperature-dependent leakage power computed.

Abstract: Provide is a method of estimating free-stream inflow at a downstream wind turbine of a wind park, the method including: selecting, from plural candidate wind turbines previously defined specifically for the downstream wind turbine, an upstream wind turbine based on a currently determined wind direction; using determination equipment of the selected upstream wind turbine to determine the free-stream inflow.

Abstract: A method of generating a matrix to relate a plurality of controllable parameters of a chemical mechanical polishing system to a polishing rate profile includes polishing a test substrate. The test substrate is polished for a first period of time using baseline parameter values with a first parameter set to a first value, and the test substrate is polished for a second period of time using first modified parameter values with the first parameter set to a modified second value. A thickness of the test substrate is monitored during polishing, and a baseline polishing rate profile is determined for the first period of time and a first modified polishing rate profile is determined for the second period of time. The matrix is calculated based on the baseline parameter values, the first modified parameters, the baseline polishing rate profile and the first modified polishing rate profile.

Abstract: The embodiments of the present disclosure provide methods, Internet of Things systems, and storage mediums for management of pipeline network inspection based on a smart gas geographic information system (GIS). The method is implemented by a smart gas pipeline network safety management platform of an Internet of Things system for management of pipeline network inspection based on the smart gas GIS, and the method comprises: obtaining inspection features of at least one inspection unit in a gas pipeline network based on the smart gas GIS; determining at least one gas inspection area by dividing a gas pipeline network map of the smart gas GIS based on the inspection features; determining an inspection result by performing an inspection based on the at least one gas inspection area; and updating the at least one gas inspection area based on the inspection result.

Abstract: Autonomous vehicle model training and validation using low-discrepancy sequences may include: generating a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points; mapping each sample of a plurality of samples of a data corpus to a corresponding entry in the low-discrepancy sequence, wherein each sample of the plurality of samples comprises one or more environmental descriptors for an environment relative to a vehicle and one or more state descriptors describing a state of the vehicle; selecting, from the data corpus, a training data set by selecting, for each multidimensional point of the low-discrepancy sequence having one or more mapped samples, a mapped sample for inclusion in the training data set; and training one or more models used to generate autonomous driving decisions of an autonomous vehicle based on the selected training data set.

Abstract: Systems, methods, and software to facilitate simulating machines used in industrial automation are disclosed herein. In at least one implementation, an API is utilized to establish at least a communication link between a simulation model created in a simulation application and an industrial controller system outside of the simulation model, wherein the simulation model comprises definitions for a virtual representation of at least a portion of a machine used in an industrial automation environment. Data is then exchanged between the industrial controller system and the simulation model over the communication link.

Abstract: Autonomous vehicle model training and validation using low-discrepancy sequences may include: generating a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points; mapping each sample of a plurality of samples of a data corpus to a corresponding entry in the low-discrepancy sequence, wherein each sample of the plurality of samples comprises one or more environmental descriptors for an environment relative to a vehicle and one or more state descriptors describing a state of the vehicle; selecting, from the data corpus, a training data set by selecting, for each multidimensional point of the low-discrepancy sequence having one or more mapped samples, a mapped sample for inclusion in the training data set; and training one or more models used to generate autonomous driving decisions of an autonomous vehicle based on the selected training data set.

Abstract: A peridynamic method having an added mirroring node according to embodiments of the present invention includes: a first step of calculating a shape tensor of a first node; a second step of calculating force state vectors of the first node and each of a plurality of second nodes by using the shape tensor; and a third step of calculating a peridynamic motion equation of the first node by using the force state vectors. The first node is a node located on a boundary of a structure and has a predetermined size horizon region, the plurality of second nodes is nodes in the horizon region, the plurality of second nodes includes one or more third nodes, and the third node is a second node having no node at a point which is origin-symmetrical based on the first node among the plurality of second nodes. In the first step, the shape tensor is calculated by using a position value in which the third node is origin-symmetrical based on the first node.

Abstract: Disclosed a method for predicting permeability of a multi-mineral phase digital core based on deep learning. In the present disclosure, a three-dimensional digital core is constructed and a pore structure is randomly generated; after a plurality of multi-mineral digital core images is acquired by performing image segmentation on the three-dimensional digital core, permeability corresponding to each of the multi-mineral digital core images is acquired by simulation using multi-physics field simulation software and a multi-mineral digital core data set is constructed based on the plurality of multi-mineral digital core images and the permeability corresponding to each of the multi-mineral digital core images; an SE-ResNet18 convolutional neural network is constructed and trained with the multi-mineral digital core data set; and an image of a multi-mineral core to be predicted is input into the trained SE-ResNet18 convolutional neural network to determine the permeability of the multi-mineral core.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: Aspects of the subject technology relate to systems and methods for controlling a hydraulic fracturing job.