Abstract: A nondestructive method for detecting damage in parts and/or characterizing effective material properties may include: exposing a material to one or more nondestructive stimuli; measuring a response of the material to the stimuli; selecting at least one of a specific length scale or a specific time scale; and analyzing the measurement of the response with a scale-aware single- or multi-physics model to identify anomalies in the measurements as compared to an expected response of the material to the stimuli, wherein the scale-aware single- or multi-physics model is based on the at least one of the specific length scale or the specific time scale.

Abstract: Water removal from humidified air and other water-laden gases may be complicated due to the need for large and expensive capital equipment or use of powder-form desiccants that may lead to pressure drops, poor throughput and energy-intensive recovery of water. Reversible water-absorbing constructs may alleviate these difficulties and comprise: a phase change polymer exhibiting a reversible hydrophilic-hydrophobic phase transition, and at least one additional material in contact with the phase change polymer, such as a water-sorptive material. The phase change polymer and the at least one additional material are formed as a plurality of filaments. Filaments formed from the phase change polymer may be generated through an electrospinning process, which may be arranged in various higher level constructs, such as in fibers, fabrics and non-woven filament mats capable of absorbing water from humidified air or other water-laden gases.

Abstract: A computer-implemented system and method for providing contextually relevant task recommendations to qualified users is provided. Contextual data for a user is collected via one more sensors associated with the user. An activity being performed by the user is determined based on the contextual data. The activity is compared with a profile of the user and a determination that the user is qualified to perform the activity is verified. The activity is monitored. At least one recommendation for performance of a step is identified during the activity and transmitted to at least one recipient including the user or another user.

Abstract: Methods may comprise: identifying a fault indicator associated with a physical system; collecting first data related to a state of the physical system; applying a surrogate model to the first data to produce a plurality of potential fault modes; applying an optimization algorithm to the plurality of potential fault modes using a similarity metric to produce an input and a plurality of outputs, wherein each of the plurality of outputs corresponds to one of the plurality of potential fault modes, wherein the input provides differentiation between each of the plurality of outputs; applying the input to the physical system; collecting second data from physical system in response to applying the input; identifying a true mode of the physical system based on a comparison of the second data and the plurality of outputs; and diagnosing a fault of the physical system based on the true mode.

Abstract: Sensors for detecting a formaldehyde-containing gas may include a first electrode and a second electrode; and an arylphosphine nanomaterial construct disposed between the first electrode and the second electrode, the arylphosphine nanomaterial construct including single-walled carbon nanotubes; and an arylphosphine including at least one aryl group. Methods for detecting formaldehyde include exposing a sensor to a formaldehyde-containing gas, the sensor including a first electrode and a second electrode; and an arylphosphine nanomaterial construct disposed between the first electrode and the second electrode, the arylphosphine nanomaterial construct including single-walled carbon nanotubes; and an arylphosphine containing at least one aryl group; monitoring a resistance between the first electrode and the second electrode; and determining a formaldehyde concentration from the resistance.

Abstract: A system and method for robust estimation of state parameters from internal readings in a sequence of images are provided. Various techniques can be implemented to address observation noise and/or underlying process noise to stabilize the readings.

Abstract: A system captures, by a recording device, a scene with physical objects, the scene displayed as a three-dimensional (3D) mesh. The system marks 3D annotations for a physical object and identifies a mask. The mask indicates background pixels corresponding to a region behind the physical object. Each background pixel is associated with a value. The system captures a plurality of images of the scene with varying features, wherein a respective image includes: two-dimensional (2D) projections corresponding to the marked 3D annotations for the physical object; and the mask based on the associated value for each background pixel. The system updates the value of each background pixel with a new value. The system trains a machine model using the respective image as generated labeled data, thereby obtaining the generated labeled data in an automated manner based on a minimal amount of marked annotations.

Abstract: An augmented data set is generated by combining chroma key substitutions for background and occlusion augmentations for foreground to enable training of object detectors or action recognition that transfers to multiple environments and is robust to occlusion due to other objects or human hands.

Abstract: A system and method for reading digits using VGG-16 backbone are provided to create visual features followed by two layers of non-linear fully connected units which are then fed to 8 categorical symbol units and a single linear length unit. The 8 categorical units provide an ordered representation of the numerical reading with required punctuation such as decimal points or colons. Training on synthetic digits followed by augmentations to create a robust detector are implemented without the need for real-world training data.

Abstract: A system and a method for secure control of a physical system are described. During operation, the system can obtain measurement of one or more sensors associated with the physical system. The system then estimates a state of the physical system based on the measurement of the sensors. Subsequently, the system generates a feedback control signal based on the estimated state and generates a watermarking signal based on a stored estimated state of the physical system at a previous time instant. The system then generates a watermarked control signal by combining the feedback control signal and the watermarking signal and applies the watermarked control signal to the physical system to regulate the state of the physical system, thereby facilitating secure control of the physical system.

Abstract: An approach to automating hybrid manufacturing is provided. This approach first splits an item of manufacture into irreducible pieces called atoms, then extracts constraints between the atoms, and then uses a planner to find the best way to assemble the atoms into the item of manufacture given the constraints. The approach splits the item into atoms based on various properties such as the presence or absence of support, the need for scaffolding, tool reachability constraints, and the presence of cycles of dependencies which prevent the item from being manufactured.

Abstract: A modification of a topology optimization formulation for part-level generation is provided such that it includes additional constraints that ensure that the inertial quantities (e.g., COM, Mol, and mass) of an automatically generated part are equivalent to specified targets. These targets are the inertial values used in an initial assembly-level simulation. The reaction and external forces acting on the part of interest resulting from assembly-level simulation provide the boundary conditions used for part-level generation. By constraining the inertial properties in this way, there is no need to evaluate the assembly-level dynamics each time a part is generated.

Abstract: One embodiment can provide a method and system for estimating a remote quantity of interest (QoI). During operation, the system can receive, over a communication channel, a radio frequency (RF) signal carrying an estimate of the QoI measured by a sensor. The system can estimate probability distributions of a set of random channel parameters associated with the HF communication channel. The system can further reconstruct the estimate based on the probability distributions of the channel parameters and the received RF signal, determine a level of uncertainty associated with the reconstructed estimate, and combine reconstructed estimates from multiple sensors based on the determined level of uncertainty associated with each reconstructed estimate to output a combined estimate of the QoI.

Abstract: Methods comprising: evaporating a catalyst source to produce a catalyst gas; condensing the catalyst gas to produce a catalyst vapor comprising catalyst droplets suspended in a gas phase; and contacting the catalyst vapor with a hydrocarbon gas to catalyze a decomposition reaction of the hydrocarbon gas into hydrogen gas and carbon. And, systems comprising: a catalyst source evaporator that provides a first stream to a reactor; a hydrocarbon source that provides a second stream to the reactor; a cooling column coupled to the reactor via a third stream comprising hydrogen, catalyst liquid, solid carbon, optionally catalyst gas, and optionally unreacted hydrocarbon gas such that the cooling column receives the third stream from the reactor; and wherein the cooling column has effluent streams that include (a) a fourth stream that comprises hydrogen and optionally catalyst gas and (b) a fifth stream that comprises catalyst liquid.

Abstract: One embodiment of the present invention provides a system that recommends activities. During operation, the system receives a piece of content obtained from text or converted to text from speech. The system then analyzes the received content to identify any activity type, indication of willingness to participate in any type of activities, and at least one piece of temporal information, which can be implicitly and/or explicitly stated in the content, and/or one piece of location information associated with the activity type. The system further recommends one or more activities, venues, and/or services that afford or support activities for a user based on the information extracted from the content.

Abstract: One embodiment provides a method for automated design of a physical system. The method can include obtaining design requirements associated with the physical system and iteratively performing, by a computer, a reinforcement learning (RL) process and a nonlinear optimization process to generate a design solution. The RL process can generate a topology represented as a model of the physical system using a modeling language. The generated topology can specify a number of components and connections among the components. The nonlinear optimization process can determine parameters of the components in the topology based on the model and a loss function. The method can further include outputting the design solution of the physical system based on the generated topology and the determined parameters of the components, thereby facilitating construction of the physical system.

Abstract: System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

Abstract: Embodiments described herein provide a system for facilitating efficient troubleshooting for a product. During operation, the system can identify an artificial-intelligence- (AI-) based dialog model operating based on a structured representation, which can indicate sequential troubleshooting steps to be performed by a user. The system can provide a machine utterance of the dialog model corresponding to a troubleshooting step to the user. The system can then search the structured representation for a semantic match for a user utterance obtained in accordance with the dialog model from the user. If the semantic match indicates an anticipated option associated with the machine utterance, the system can traverse a current branch of the structured representation using the dialog model based on the anticipated option. Otherwise, if the semantic match indicates an option on a different branch, the system can jump to the option on the different branch for subsequent traversal.

Abstract: One embodiment provides a method and a system for reconstructing symbols transmitted over a high frequency (HF) communication channel. During operation, the system can receive, at a receiver, a radio frequency (RF) signal carrying an input data frame and transmitted over the HF communication channel. The input data frame includes a number of known symbols followed by a number of unknown symbols. The system can determine a set of channel parameters associated with the HF communication channel based on the received RF signal and the known symbols and reconstruct, using a machine-learning technique, the unknown symbols based on the determined channel parameters and the received RF signal.

Abstract: A transferable hybrid method for prognostics of engineering systems based on fundamental degradation modes is provided. The method includes developing a degradation model that represents degradation modes shared in different domains of application through the integration of physics and machine learning. The system measures sensor signals and data processing provides for extracting health indicators correlated with the fundamental degradation modes from sensors data. For the integration of physics and machine learning, the degradation mode is separated into different phases. Before the accelerated degradation phase of a system, the method is looking out to detect when the accelerated phase begins. When accelerated phase is active, the system applies a machine-learning model to provide information on the accelerated degradation phase, and evolves the degradation towards a failure threshold in a simulation of the updated physics-based model to predict the degradation progression.