Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: A second type computer assembly line balancing optimization method based on a migration genetic algorithm, and related to the technical field of assembly line balancing. The method uses assembly experience of similar assembly lines, the feasible solution set of the known assembly lines is transferred to the initial solution set of the assembly line balancing problem to be optimized, due to the migration of high-quality feasible solutions. The method can effectively reduce the sensitivity of the algorithm performance to the initial value and parameters, and improve the lower limit of the local optimal feasible solution of the heuristic algorithm to solve the assembly line balancing problem.

Abstract: Provided is a sound-based roller fault detecting method by using double-projection neighborhoods preserving embedding, including: acquiring sound data during operation of a roller, performing a wavelet transform energy feature extraction on normal data in the data to obtain wavelet transform energy feature data, then performing double-projection neighborhoods preserving embedding feature extraction on the wavelet energy feature data to obtain an optimal projection matrix of the feature data, establishing a detection model, constructing T2 statistics of a feature space and a residual space of normal sound data, determining detection control limits according to the T2 statistics by a kernel density estimation method, and further judging whether newly acquired data has faults.

Abstract: A modeling method for soft measurement of temperature of a blast furnace tuyere raceway includes: collecting picture data of flame combustion at the blast furnace tuyere raceway, physical variable data reflecting operation states of a blast furnace and combustion temperature data of the blast furnace tuyere raceway; extracting characteristics of the picture data of the flame combustion; constructing a multi-kernel least squares support vector regression model based on Pearson correlation coefficient method and least squares support vector regression algorithm as a soft measurement model; optimizing parameters of the soft measurement model by using sine cosine optimization algorithm; and taking optimal kernel function parameters of the picture data, kernel function parameters of the physical variable data and regularization parameters in the multi-kernel least squares support vector regression model as final parameters of the soft measurement model, and achieving prediction and calculation of the combustion te

Abstract: The invention provides a calculation method for operating resistance in a dual-electrode DC electric-smelting furnace for magnesium, including the following steps of: calculating a raw material resistance: simplifying a raw material model as an electrode-centered cylindrical model, determining an electric-field strength of each point in an electric field generated by a raw material layer around an electrode in the cylindrical model, calculating a raw material voltage between two electrodes according to the electric-field strength of each point in the electric field, and further obtaining the raw material resistance between the two electrodes; calculating an electric arc-resistance relation model: determining a relation between an actual electric arc length and a distance from the electrode to a surface of a smelting pool, and calculating a relation between an electric arc voltage and the actual electric arc length, namely the electric arc-resistance relation model; and calculating a smelting pool resistance,

Abstract: The invention provides a calculation method for operating resistance in a dual-electrode DC electric-smelting furnace for magnesium, including the following steps of: calculating a raw material resistance: simplifying a raw material model as an electrode-centered cylindrical model, determining an electric-field strength of each point in an electric field generated by a raw material layer around an electrode in the cylindrical model, calculating a raw material voltage between two electrodes according to the electric-field strength of each point in the electric field, and further obtaining the raw material resistance between the two electrodes; calculating an electric arc-resistance relation model: determining a relation between an actual electric arc length and a distance from the electrode to a surface of a smelting pool, and calculating a relation between an electric arc voltage and the actual electric arc length, namely the electric arc-resistance relation model; and calculating a smelting pool resistance,

Abstract: A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.

Abstract: A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.

Abstract: Provided is a fault isolation method in industrial process based on regularization framework, including the steps of: collecting and filtering sample data in industrial process to obtain an available sample data set; establishing an objective function for fault isolation in industrial process with local and global regularization items; calculating the optimal solution to the objective function for fault isolation in industrial process by the available sample data set; obtaining a predicted classification label matrix according to the optimal solution to determine the fault information in the process. The method uses the local regularization item to make the nature of the optimal solution ideal, and uses the global regularization item to correct problem of low fault isolation precision caused by the local regularization item. Experiments show that the method is not only feasible but also provides high fault isolation precision and mining the potential information of labeled sample data.

Abstract: A fault detector for operating process of electric arc furnace and method thereof are disclosed, which belong to the technical field of fault detection. The fault detector includes the temperature signal acquisition equipment, the current signal acquisition equipment and a host computer. The multimode fault monitoring and diagnosis method comprises the following steps: acquiring and standardizing the data; establishing the preliminary monitoring model for the operating process to obtain common subsets of M operating modes and typical subsets of every operating mode; calculating the T2 statistics and the SPE statistics, and monitoring and diagnosing fault in the operating process. The present invention has the advantages that the colorimetric temperature measurement can improve the calculation accuracy, different equipment becomes compatible, and the fault detector is suitable for operating in industrial production process with a variety of steady modes and can diagnose faults in a certain operating mode.

Abstract: A fault detection method in a continuous annealing process based on a recursive kernel principal component analysis (RKPCA) is disclosed. The method includes: collecting data of the continuous annealing process including roll speed, current and tension of an entry loop (ELP); building a model using the RKPCA and updating the model, and calculating the eigenvectors {circumflex over (P)}. In the fault detection of the continuous annealing process, when the T2 statistic and SPE statistic are greater than their confidence limit, a fault is identified; on the contrary, the whole process is normal. The method mainly solves the nonlinear and time-varying problems of data, updates the model and calculates recursively the eigenvalues and eigenvectors of the training data covariance by the RKPCA. The results show that the method can not only greatly reduce false alarms, but also improve the accuracy of fault detection.

Abstract: A fault detector for operating process of electric arc furnace and method thereof are disclosed, which belong to the technical field of fault detection. The fault detector includes the temperature signal acquisition equipment, the current signal acquisition equipment and a host computer. The multimode fault monitoring and diagnosis method comprises the following steps: acquiring and standardizing the data; establishing the preliminary monitoring model for the operating process to obtain common subsets of M operating modes and typical subsets of every operating mode; calculating the T2 statistics and the SPE statistics, and monitoring and diagnosing fault in the operating process. The present invention has the advantages that the colorimetric temperature measurement can improve the calculation accuracy, different equipment becomes compatible, and the fault detector is suitable for operating in industrial production process with a variety of steady modes and can diagnose faults in a certain operating mode.

Abstract: A fault detection method in a continuous annealing process based on a recursive kernel principal component analysis (RKPCA) is disclosed. The method includes: collecting data of the continuous annealing process including roll speed, current and tension of an entry loop (ELP); building a model using the RKPCA and updating the model, and calculating the eigenvectors {circumflex over (p)}. In the fault detection of the continuous annealing process, when the T2 statistic and SPE statistic are greater than their confidence limit, a fault is identified; on the contrary, the whole process is normal. The method mainly solves the nonlinear and time-varying problems of data, updates the model and calculates recursively the eigenvalues and eigenvectors of the training data covariance by the RKPCA. The results show that the method can not only greatly reduce false alarms, but also improve the accuracy of fault detection.

Abstract: An embodiment of the present invention provides an optimizer for optimizing source code to generate optimized source code having instructions for instructing a central processing unit (CPU) to iteratively compute values for a primary recurrence element. A computer programmed loop for computing the primary recurrence element and subsequent recurrence elements is an example of a case involving iteratively computing the primary recurrence element. The CPU is operatively coupled to fast operating memory (FOM) and operatively coupled to slow operating memory (SOM). SOM stores the generated optimized source code. The optimized source code includes instructions for instructing said CPU to store a computed value of the primary recurrence element in a storage location of FOM. The instructions also includes instructions to consign the computed value of the primary recurrence element from the storage location to another storage location of the FOM.

Abstract: An embodiment of the present invention provides an optimizer for optimizing source code to generate optimized source code having instructions for instructing a central processing unit (CPU) to iteratively compute values for a primary recurrence element. A computer programmed loop for computing the primary recurrence element and subsequent recurrence elements is an example of a case involving iteratively computing the primary recurrence element. The CPU is operatively coupled to fast operating memory (FOM) and operatively coupled to slow operating memory (SOM). SOM stores the generated optimized source code. The optimized source code includes instructions for instructing said CPU to store a computed value of the primary recurrence element in a storage location of FOM. The instructions also includes instructions to consign the computed value of the primary recurrence element from the storage location to another storage location of the FOM.

Abstract: An embodiment of the present invention provides an optimizer for optimizing source code to generate optimized source code having instructions for instructing a central processing unit (CPU) to iteratively compute values for a primary recurrence element. A computer programmed loop for computing the primary recurrence element and subsequent recurrence elements is an example of a case involving iteratively computing the primary recurrence element. The CPU is operatively coupled to fast operating memory (FOM) and operatively coupled to slow operating memory (SOM). SOM stores the generated optimized source code. The optimized source code includes instructions for instructing said CPU to store a computed value of the primary recurrence element in a storage location of FOM. The instructions also includes instructions to consign the computed value of the primary recurrence element from the storage location to another storage location of the FOM.