Abstract: Genome-wide data is obtained, and data cleansing, data sparsity processing and bioinformatics feature extraction are performed on the obtained genome-wide data; model construction is performed based on the sparsity-processed genome-wide data and the bioinformatics features to obtain a preliminary hybrid model; model training, regularization, and interpretability enhancement are performed on the preliminary hybrid model to obtain a trained model weight and an interpretability analysis corresponding to the trained model weight; learning and uncertainty estimation are performed based on the trained model weight and to-be-predicted genome-wide data on the hybrid model to obtain an integrated prediction result and uncertainties corresponding to the integrated prediction result; and personalized medical advice and decision assistance are performed based on the integrated prediction result, the interpretability analysis, and the uncertainties corresponding to the integrated prediction result.

Abstract: Provided is a method for detecting abnormal working conditions of multi-view data based on feature regression. By the method, data capable of being acquired in a production process is collected together, a big data pool is established, and historical data information is fully utilized; by analyzing the data in the data pool, the method for detecting abnormal working conditions based on the multi-view data is established by a feature regression method, and a general mathematical model is established for preprocessed data acquired by different sensors; left and right projection vectors solved through the model can make similar sample points have better clustering effects in a low dimensional space; and by comparing a correlation between vectors after dimensionality reduction and various category vectors, production working conditions at a current time can be recognized.

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

Abstract: Provided is a calculating and real-time monitoring method for a boundary of a blast furnace tuyere raceway including: the steps of firstly establishing a depth calculation model for the raceway, and further obtaining a calculation formula for a depth of the raceway so as to obtain a change law of the depth of the raceway; establishing a boundary model for the raceway through the depth calculation model for the raceway, and determining a calculation formula for the boundary of the raceway; obtaining modelling parameters, analyzing an impact of the modelling parameters on the boundary model, and determining main parameters that affect the boundary of the raceway; finally, solving a height of the raceway; and when the height or depth of the raceway exceeds a set range, adjusting a blast wind pressure and a blast wind volume to restore the height or depth of the raceway to a normal range.

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.