Abstract: An inverter and an integrated platform having the same are provided. The inverter includes a cabinet having a cabinet door; an inverter body arranged in the cabinet; and a heat exchanger arranged on an outer side of the cabinet, where the inverter body has an alternating-current side, and the cabinet door is arranged on the alternating-current side or an opposite side of the alternating-current side of the inverter body.

Abstract: A method for detecting an anomaly in time series sensor data. The method may include identifying a noisiest cycle from the time series sensor data; for an evaluation of the noisiest cycle indicative of the anomaly being detected at a confidence level above a threshold, providing an output associated with the noisiest cycle as being the anomaly; and for the evaluation of the noisiest cycle indicative of the anomaly being detected at the confidence level not above the threshold: identifying a cycle from the time series sensor data having a most differing shape; and providing the output associated with the cycle having the most differing shape as being the anomaly.

Abstract: An apparatus for predicting a characteristic of a system is provided. The apparatus may include a memory and at least one processor coupled to the memory. The at least one processor may be configured to perform a method including measuring, at a high sample rate, data relating to an operation of the system over a first time period. The method may further include producing a two-dimensional (2D) time-and-frequency input data set by applying a wavelet transform to the measured data. The method may additionally include generating a set of one or more values associated with one or more system characteristics by processing the 2D time-and-frequency input data set using a functional neural network (FNN).

Abstract: Systems and methods described herein can involve executing a functional generator configured to generate multivariate continuous sensor curves from training with arbitrary multivariate sensor data with irregular timestamps received from one or more apparatuses; executing a functional discriminator to discriminate the generated multivariate continuous sensor curve from the arbitrary multivariate sensor data; and for the functional discriminator discriminating the generated multivariate continuous sensor curve from the arbitrary multivariate sensor data with irregular timestamps, providing feedback to the functional generator to retrain the functional generator.

Abstract: In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.

Abstract: A device for analyzing cell morphology and a method for identifying cells are provided. A digital camera photographs a cell image of a blood sample under a low-magnification objective lens. A processor identifies and positions suspected cells of preset type in the cell image to obtain an identification result. Based on the identification result and a target number, the processor determines a number of suspected cells of preset type to be identified and positioned under the low-magnification objective lens. The digital camera further photographs, under a high-magnification objective lens, the suspected cells of preset type identified and positioned, and then the processor identifies whether the suspected cells of preset type photographed are cells of preset type, to count the number of cells of preset type photographed under the high-magnification objective lens and obtain a statistical value. If the statistical value?the target number, photographing is stopped.

Abstract: Aspects of the present disclosure are directed to systems and methods for determining execution of failure prediction models and duration prediction models for a sensor system. Systems and methods can involve receiving streaming data from one or more sensors and for a failure prediction model processing the streaming data indicating a predicted failure with a probability higher than a threshold, obtaining a duration of the predicted failure from a duration prediction model configured to predict durations of detected failures based on the streaming data; deactivating the failure prediction model when the predicted failure occurs; and determining a time to reactivate the failure prediction model based on the obtained duration of the predicted failure.

Abstract: In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.

Abstract: Example implementations described herein involve systems and methods involving a plurality of sensors monitoring one or more processes, the sensors providing sensor data, which can include determining a probability map of the sensor data from a database and a functional relationship between key performance indicators (KPIs) of the one or more processes and the sensor data; executing a search on the probability map to determine constrained and continuous ranges for the sensor data that optimize KPIs for the one or more processes based on the functional relationship; and generating a recommendation for the one or more processes that fit within the constrained and continuous range of the sensor data.

Abstract: Example implementations described herein involve systems and methods involving a plurality of sensors monitoring one or more processes, the sensors providing sensor data, which can include determining a probability map of the sensor data from a database and a functional relationship between key performance indicators (KPIs) of the one or more processes and the sensor data; executing a search on the probability map to determine constrained and continuous ranges for the sensor data that optimize KPIs for the one or more processes based on the functional relationship; and generating a recommendation for the one or more processes that fit within the constrained and continuous range of the sensor data.

Abstract: Example implementations described herein are directed to constructing prediction models and conducting predictive maintenance for systems that provide sparse sensor data. Even if only sparse measurements of sensor data are available, example implementations utilize the inference of statistics with functional deep networks to model prediction for the systems, which provides better accuracy and failure prediction even if only sparse measurements are available.

Abstract: Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

Abstract: Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

Abstract: A method and a system for controlling quality of service of a storage system, and a storage system. The method includes: collecting information about processing capabilities of the hard disks in the storage system and obtaining processing capabilities of the hard disks according to the information about processing capabilities; dividing a cache into multiple cache tiers according to the processing capabilities of the hard disks; and writing, for a cache tier in which dirty data reaches a preset threshold, data in the cache tier into at least one hard disk corresponding to the cache tier. The method avoids a phenomenon of preempting page resources in the cache.

Abstract: A method and a system for controlling quality of service of a storage system, and a storage system. The method includes: collecting information about processing capabilities of the hard disks in the storage system and obtaining processing capabilities of the hard disks according to the information about processing capabilities; dividing a cache into multiple cache tiers according to the processing capabilities of the hard disks; and writing, for a cache tier in which dirty data reaches a preset threshold, data in the cache tier into at least one hard disk corresponding to the cache tier. The method avoids a phenomenon of preempting page resources in the cache.