Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: A method of managing real-time electrical energy storage management from an electricity storage device to maximise the reduction of the use of a mains electricity grid during peak cost charging periods on the mains electricity grid, including: calculating an ideal offline use of the electricity storage device using ideal predicted parameters; calculate a plurality of electrical storage device storage and electrical discharge models using a plurality of algorithms based on recorded data; calculate the competitive ratio for each of the algorithms and the ideal offline use; and use power from the electricity storage device based on an optimal competitive ratio.

Abstract: A method of adjusting the inputs to an industrial process, which can be any process such as the chemical, pharmaceutical, food processes and so on, comprising modelling the process output based on the input variables using regression techniques. The method provides for pre-identified variables not to be subjected to the penalty while the other variables may be penalised to zero by the regression techniques. This provides a model which can discriminate between the variables.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: A method for extracting latent vector autoregressive (LaVAR) models with full interactions amongst mutually independent dynamic latent variables (DLV) from multi-dimensional time series data comprising: detecting, by a plurality of sensors, dynamic samples of data corresponding to a plurality of original variables; analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data; estimating an estimated current value for all of the latent variables, wherein the estimation for all of the latent values is conducted simultaneously through an iterative process; and wherein each of the latent variables are contemporaneously uncorrelated.

Abstract: A stabilization method and mechanism for model structure learning is described. A model is built based on a full data set. The full data set is partitioned into cross validation (CV) folds. A set of model structures of the model are cross validated for each CV fold while penalizing structural deviations from the model to determine CV errors. A model structure is selected from the set of model structures based on a comparison of CV errors with an industrial data set.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: A method, computer system, and a computer program product for designing one or more folded structural proteins from at least one raw amino acid sequence is provided. The present invention may include computing one or more character embeddings based on the at least one raw amino acid sequence by utilizing a multi-scale neighborhood-based neural network (MNNN) model. The present invention may then include refining the computed one or more character embeddings with at least one set of sequence neighborhood information. The present invention may further include predicting one or more dihedral angles based on the refined one or more character embeddings.

Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables.

Abstract: A method for monitoring a control of a parameter of one or more devices or systems in an oil or gas production site includes receiving process data, the process data being a result of the control of the parameter of the one or more devices or systems in the production site; smoothing the process data using a polynomial filter while preserving features of the process data to obtain smoothed data; and applying a pattern recognition algorithm to the smoothed data to determine whether there is a malfunction condition in the one or more devices or systems.

Abstract: A computer-implemented method for reconstructing data includes receiving a selection of one or more input data streams at a data processing framework. The method can include determining existence of a fault in the input data stream(s). This determination can be based on receiving a definition of one or more analytics components at the data processing framework and applying a dynamic principal component analysis (DPCA) to the input data streams. Detection of the fault can be based at least in part on a prediction error and a variation in principal component subspace generated based on the DPCA. Detection of the fault can also be based on performing a wavelet transform to generate a set of coefficients defining the data stream, the set of coefficients including one or more coefficients representing a high frequency portion of data included in the data stream. The method can include reconstructing data at the fault.

Abstract: Methods and systems for reconstructing data are disclosed. One method includes receiving a selection of one or more input data streams at a data processing framework, and receiving a definition of one or more analytics components at the data processing framework. The method further includes applying a dynamic principal component analysis to the one or more input data streams, and detecting a fault in the one or more input data streams based at least in part on a prediction error and a variation in principal component subspace generated based on the dynamic principal component analysis. The method also includes reconstructing data at the fault within the one or more input data streams based on data collected prior to occurrence of the fault.

Abstract: The invention discloses a method to control the work of electronic thermometer by using the position of probe. The probe incased in a disposable sheath, wherein the pre-heating mechanism in the probe and the control switch is connected with the host of the electronic thermometer by wires; and by using the axial reciprocating motion of the probe to trigger the control switch to initiate the electronic thermometer. When using the invention, we use the movement for the assembling of the sheath to initiate the hearting process and measurement, to ensure that the disposable sheath is assembled on the probe, and then execute the measurement, so as to eliminate the possibility of cross infection completely. The invention also exempts the requirement for the users to the return back of the probe into the cavity after use, and brings convenience for the users: Meanwhile, the equipment structure is simple, and the machinery assembles are few by comparison, which effectively decrease the production cost.

Abstract: The invention discloses an induction type of electronic thermometer probe motion detection device, including a probe and a probe holder; wherein the probe includes a cavity and a probe tip; Inside the probe tip has a heating mechanism, wherein the probe tip and the heating mechanism electrically connected with the host of the electronic thermometer by wires; Inside the cavity locates a magnetic inductive switch, wherein the magnetic inductive switch communicates with the host of electronic thermometer by wires; A magnetic signal source located in the position of the other side of the probe holder after the placement of the probe, where corresponds to the magnetic inductive switch, and then the magnetic signal source and the magnetic inductive switch composed to the probe motion detection device.

Abstract: The invention discloses an induction type of electronic thermometer probe motion detection device, including a probe and a probe holder; wherein the probe includes a cavity and a probe tip; Inside the probe tip has a heating mechanism, wherein the probe tip and the heating mechanism electrically connected with the host of the electronic thermometer by wires; Inside the cavity locates a magnetic inductive switch, wherein the magnetic inductive switch communicates with the host of electronic thermometer by wires; A magnetic signal source located in the position of the other side of the probe holder after the placement of the probe, where corresponds to the magnetic inductive switch, and then the magnetic signal source and the magnetic inductive switch composed to the probe motion detection device.

Abstract: The invention discloses a method to control the work of electronic thermometer by using the position of probe. The probe incased in a disposable sheath, wherein the pre-heating mechanism in the probe and the control switch is connected with the host of the electronic thermometer by wires; and by using the axial reciprocating motion of the probe to trigger the control switch to initiate the electronic thermometer. When using the invention, we use the movement for the assembling of the sheath to initiate the hearting process and measurement, to ensure that the disposable sheath is assembled on the probe, and then execute the measurement, so as to eliminate the possibility of cross infection completely. The invention also exempts the requirement for the users to the return back of the probe into the cavity after use, and brings convenience for the users: Meanwhile, the equipment structure is simple, and the machinery assembles are few by comparison, which effectively decrease the production cost.