Abstract: One or more structural equations modeling a physical process over time may be sampled using simulated parameter values to generate input data signal values. A noise generator may be applied to the input data signal values to generate noise values. The noise values and the input data signal values may be combined to determined noisy data signal values. These noisy data signal values may in turn be used in combination with one or more states to train a prediction model.

Abstract: An agricultural tool includes a motor, a first driving gear to be driven by the motor, a second driving gear to be driven by the motor, and a main gear including a plurality of openings along a periphery of the main gear. The plurality of openings include a first opening and a second opening. The main gear includes a first receiving portion that defines a first receiving space, and a second receiving portion that defines a second receiving space. The first opening corresponds and is attached to the first receiving space, and the second opening corresponds and is attached to the second receiving space. The first driving gear and the second driving gear engage with the periphery of the main gear to drive the main gear.

Abstract: A robotic arm mount assembly includes a first mount with a main body, the main body including a first end and a second end opposed to the first end in an axial direction, the first end including a first mounting portion. The robotic arm mount assembly also includes a motor including a motor body and a motor shaft attached to the motor body, a pinion gear attached to the motor shaft, a rack to be driven by the pinion gear, and a second mount including an extension portion and a second mounting portion.

Abstract: An agricultural tool includes a motor, a magazine to house one or more clips, and a sliding arm to be moved by the motor between a retracted position and a deployed position. The sliding arm moves between the retracted position and the deployed position to attach a clip, from among the one or more clips, to the agricultural item of interest and the support structure to fasten or attach the agricultural item of interest to the support structure.

Abstract: An agricultural clip includes a base portion, a left arm portion attached to the base portion, a right arm portion attached to the base portion, and a curved protrusion. The left arm portion and the right arm portion define a clip receiving space to receive the agricultural item of interest and the support structure, and the curved protrusion includes a first end attached to one of the left arm portion and the right arm portion, and a second end which is a free end that protrudes into the clip receiving space.

Abstract: A method for fastening or attaching an agricultural item of interest to a support structure includes housing one or more clips in a magazine, and moving a sliding arm, which is driven by a motor, between a retracted position and a deployed position to attach a clip, from among the one or more clips, to the agricultural item of interest and the support structure to fasten or attach the agricultural item of interest to the support structure.

Abstract: A cutting device includes a base plate, a motor attached to the base plate, a driving cord including a first end and a second end, the first end being attached to the motor, and a cutting assembly. The cutting assembly includes a fixed blade that is fixed to the base plate, and a driven blade that moves with respect to the fixed blade. The second end of the driving cord is attached to the driven blade to move the driven blade with respect to the fixed blade when the motor rotates.

Abstract: A method for tying/twisting an agricultural item of interest and a support structure together includes capturing the agricultural item of interest with an agricultural tool, capturing the support structure with the agricultural tool, and tying/twisting the agricultural item of interest and the support structure together such that the agricultural item of interest is supported by the support structure.

Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.

Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

Abstract: A first plurality of predictor values occurring during or before a first time interval may be received. An estimated outcome value may be determined for a second time interval by applying a prediction model via a processor to the first plurality of predictor values. A designated outcome value occurring during the second time interval and a second plurality of predictor values occurring during or before the second time interval may be received. An error value may be determined based on the estimated outcome value and the designated outcome value. A drift value for a second time interval may be determined by fitting a function to the second plurality of predictor values. The prediction model may be updated when it is determined that the drift value exceeds a designated drift threshold or that the error value exceeds a designated error threshold.

Abstract: A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.

Abstract: In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.

Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.

Abstract: Described herein are methods and systems for remote charging of work vehicles using recharge vehicles. A recharge vehicle is equipped with power storage that has a sufficient capacity for propelling the recharge vehicle between a charging station (used for charging the recharge vehicle) and a work location (of the work vehicle) and also for charging the work vehicle at the work location. In some examples, the charging of the work vehicle and even the connection between the vehicles are formed while the work vehicle continues to operate. This approach helps to maximize the operating time of the work vehicle. Furthermore, this approach relaxes the charge rate requirement for charging the recharge vehicle and also for charging the work vehicle as well as the location of the charging station (for charging the recharge vehicle). In some examples, work vehicles and/or recharge vehicles are autonomous vehicles and use vehicle-to-vehicle coordination features.

Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

Abstract: Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.

Abstract: A method of processing data for an artificial intelligence (AI) system includes extracting features of the data to produce a lower dimensional representation of the data points; grouping the lower dimensional representation into clusters using a clustering algorithm; comparing the classes of data points within the clusters; and identifying unrepresented, under-represented, or misrepresented data.

Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.