Abstract: The present disclosure provides certain fused tricyclic ring derivatives that are Src Homology-2 phosphatase (SHP2) inhibitors and are therefore useful for the treatment of diseases treatable by inhibition of SHP2. Also provided are pharmaceutical compositions containing such compounds and processes for preparing such compounds.

Abstract: A method and a system of lithium battery state of charge (SOC) estimation based on second-order difference particle filtering belonging to the technical field of battery management are provided. The method includes the following steps: building a second-order RC battery model of a lithium battery; performing model parameterization by using a least squares algorithm with a forgetting factor; and generating an importance density function through a second-order central difference Kalman filtering (SCDKF) algorithm, improving a particle filtering algorithm to obtain a second-order difference particle filtering (SCDPF) algorithm, and performing SOC estimation on a lithium battery by using the SCDPF. The estimation method provided by the disclosure is accurate and has greater estimation accuracy than an unscented particle filtering algorithm (UPF), an unscented Kalman filtering algorithm (UKF), and an extended Kalman filtering algorithm (EKF). An SOC value of the lithium battery may thus be accurately estimated.

Abstract: The disclosure provides a deep parallel fault diagnosis method and system for dissolved gas in transformer oil, which relate to the field of power transformer fault diagnosis. The deep parallel fault diagnosis method includes: collecting monitoring information of dissolved gas in each transformer substation and performing a normalizing processing on the data; using the dissolved gas in the oil to build feature parameters as the input of the LSTM diagnosis model, and performing image processing on the data as the input of the CNN diagnosis model; building the LSTM diagnosis model and the CNN diagnosis model, respectively, and using the data set to train and verify the diagnosis models according to the proportion; and using the DS evidence theory calculation to perform a deep parallel fusion of the outputs of the softmax layers of the two deep learning models.

Abstract: A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

Abstract: A method and a system for diagnosing an open circuit (OC) fault of an insulated gate bipolar transistor (IGBT) of a T-type three-level (T23L) inverter under multiple power factors based on instantaneous current distortion are provided. Similar characteristics of current distortion may be caused by an OC fault of a T23L inverter, making it is difficult to locate the fault. The method for diagnosing an OC fault of a grid-connected T23L inverter, can diagnose the OC fault hierarchically; four switch transistors in a phase can be divided into two groups according to the similarity analysis of current distortion under different power factors; group-based fault diagnosis is realized by half cycles in which a zero domain occurs; and then, a specific switching signal is injected to realize equipment-based OC fault diagnosis. The OC fault diagnosis of a T23L inverter is realized without additional hardware circuits.

Abstract: Disclosed herein are processes for preparing certain intermediates useful in the synthesis of 3-fluoro-5-(((1S,2aR)-1,3,3,4,4-pentafluoro-2a-hydroxy-2,2a,3,4-tetrahydro-1H-cyclopenta[cd]inden-7-yl)oxy)benzonitrile or a pharmaceutically acceptable salt thereof.

Abstract: A transformer health state evaluation method based on a leaky-integrator echo state network includes the following steps: collecting monitoring information in each substation; performing data filtering, data cleaning and data normalization on the collected monitoring information to obtain an input matrix; inputting the input matrix into a leaky-integrator echo state network to generate trainable artificial data, and dividing the artificial data into a training set and a test set in proportion; constructing a deep residual neural network based on a squeeze-and-excitation network, and inputting the training set and the test set for network training; and performing health state evaluation and network weight update based on actual test data. Considering that a deep learning-based neural network needs a large amount of data, the present disclosure uses the leaky-integrator echo state network to generate the artificial training data.

Abstract: The present disclosure provides certain processes of making 3-fluoro-5-(((1S,2aR)-1,3,3,4,4-pentafluoro-2a-hydroxy-2,2a,3,4-tetrahydro-1H-cyclopenta[cd]inden-7-yl)oxy)-benzonitrile (Compound 1) and certain polymorphs thereof. Also provided are pharmaceutical compositions comprising a crystalline polymorph form of Compound 1 and processes for preparing such polymorph forms.

Abstract: A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.

Abstract: A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result.

Abstract: The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results.

Abstract: A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage.

Abstract: The disclosure provides a wireless channel scenario identification method and system. The method includes: simulating different wireless channel scenarios to obtain a channel scenario baseband signal y(t)pq; extracting a feature parameter of y(t)pq, extracting an autocorrelation function Ah(t)pq and performing a Fourier transform thereon to obtain a power spectral density function S(t)pq; normalizing S(t)pq to obtain a normalized channel scenario power spectral density function S(t)pq; designing a deep learning network and inputting S(t)pq and a category label pair to train the deep learning network; and for a system with a channel scenario to be identified, collecting a passband signal at its receiving end, obtaining the normalized scenario power spectral density function ?(t)pq, and using ?(t)pq as an input of the trained classifier, the output of the classifier being a label sequence of the channel scenario, and the channel scenario is effectively determined.

Abstract: The invention discloses a method and a system for correction of the junction temperatures of an IGBT module in a photovoltaic inverter.

Abstract: A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.

Abstract: A fault diagnosis method for power electronic circuits based on optimizing a deep belief network, including steps. (1) Use RT-LAB hardware-in-the-loop simulator to set up fault experiments and collect DC-link output voltage signals in different fault types. (2) Use empirical mode decomposition to extract the intrinsic function components of the output voltage signal and its envelope spectrum and calculate various statistical features to construct the original fault feature data set. (3) Based on the feature selection method of extreme learning machine, remove the redundancy and interference features, as fault sensitive feature data set. (4) Divide the fault sensitive feature set into training samples and test samples, and primitively determine the structure of the deep belief network. (5) Use the crow search algorithm to optimize the deep belief network. (6) Obtain the fault diagnosis result.

Abstract: The disclosure discloses a multi-time-scale reliability evaluation method of a wind power IGBT considering fatigue damage and a system thereof. Lifetime information of a power device is comprehensively extracted by using multiple time scales. An electro-thermal coupling model of an IGBT module is established to obtain a junction temperature data. A steady-state junction temperature database of the IGBT in different aging states is established. Based on a SCADA monitoring data, the junction temperature data is outputted in real-time through the electro-thermal coupling model and a real-time thermal stress cycle number is calculated in a short-term time-scale profile, and a wind speed probability distribution curve is obtained in a long-term time-scale profile. A maximum thermal stress cycle number that the IGBT can withstand in different aging stages is obtained in advance and a cumulative damage degree and an estimated lifetime of the IGBT of the wind power converter are calculated.

Abstract: A method for estimating the junction temperature on-line on an insulated gate bipolar transistor (IGBT) power module, including the following steps. Estimate the junction temperature by the temperature sensitive electrical parameter method, set the space thermal model of the extended state, and apply the Kalman filter to the junction temperature estimation. The temperature sensitive electrical parameter method estimates the junction temperature of the IGBT power module in real time, selects the IGBT conduction voltage drop VCE(ON) as the temperature sensitive electrical parameter, and provides a VCE(ON) on-line measuring circuit. The power loss of the diode and IGBT and the estimated value of junction temperature obtained by the temperature sensitive electrical parameter method are taken as the input of the Kalman filter, and measurement noise and process noise are considered to obtain an optimal estimated value of junction temperature.

Abstract: A method and a system for power equipment diagnosis based on windowed feature and Hilbert visualization are provided, which belong to the field of power equipment fault diagnosis. The method includes: obtaining an original data set of monitoring data containing power equipment fault features; introducing windowed feature calculation considering logarithmic constraints to process data to obtain a feature sequence; using Hilbert visualization method for further processing to obtain a Hilbert image data set used to train and verify a convolutional neural network; and finally directly inputting newly obtained test sample data after windowed feature calculation and Hilbert visualization processing into the trained network for fault diagnosis and location.

Abstract: The present disclosure provides certain fused tricyclic ring derivatives that are Src Homology-2 phosphatase (SHP2) inhibitors and are therefore useful for the treatment of diseases treatable by inhibition of SHP2. Also provided are pharmaceutical compositions containing such compounds and processes for preparing such compounds.