Abstract: The present invention relates to a benzo seven-membered ring bifunctional compound and an application thereof, and in particular to a compound represented by formula (IV) and a pharmaceutically acceptable salt thereof. The compound can be used for preparing a drug for treating diseases related to an estrogen receptor protein degradation targeting chimera.

Abstract: An indoline compound. Specifically disclosed is an application of a compound represented by formula (I) and pharmaceutically acceptable salts thereof in the preparation of drugs for treating related diseases.

Abstract: Provided are a class of benzimidazole compounds and an application thereof as a 300/CBP inhibitor. Specifically, provided are a compound represented by formula (P) and a pharmaceutically acceptable salt thereof.

Abstract: Provided is an aldehyde binder, specifically, disclosed is a compound as represented by formula (II) or a pharmaceutically acceptable salt.

Abstract: The present invention relates to a class of TrkA inhibitors and an application thereof in the preparation of a drug for the treatment of diseases associated with TrkA. The present invention specifically discloses compounds represented by formula (I) and formula (II), tautomer thereof or pharmaceutically acceptable salts thereof.

Abstract: Disclosed are a [1,2,4]triazolo[1,5-a]pyridine compound as JAK inhibitor and an application thereof in preparing a drug for treating a disease related to JAK1 or/and TYK2. Specifically, the present invention relates to a compound represented by formula (I), or an isomer or pharmaceutically acceptable salt thereof.

Abstract: A class of lactam-modified polypeptide compounds and the use thereof in the preparation of a drug for treating related diseases.

Abstract: Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

Abstract: A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special technique is developed to generate neural network training data, that is particularly suitable and favorable, to develop a high-performance neural network-based IPM torque control system, and the impact of variable motor parameters is embedded into the neural network system development and training. The provided method can achieve a fast and accurate current reference generation with a simple neural network structure, for optimal torque control of an IPM motor. The method can handle the MTPA, MTPV, and flux-weakening operation considering physical motor constraints.

Abstract: An example method for controlling a DC/DC converter or a standalone DC microgrid comprises an artificial neural network (ANN) based control method integrated with droop control. The ANN is trained to implement optimal control based on approximate dynamic programming. In one example, Levenberg-Marquardt (LM) algorithm is used to train the ANN, where the Jacobian matrix needed by LM algorithm is calculated via a Forward Accumulation Through Time algorithm. The ANN performance is evaluated by using power converter average and switching models. Performance evaluation shows that a well-trained ANN controller has a strong ability to maintain voltage stability of a standalone DC microgrid and manage the power sharing among the parallel distributed generation units. Even in dynamic and power converter switching environments, the ANN controller shows an ability to trace rapidly changing reference commands and tolerate system disturbances, and operate the DC/DC converter or the microgrid in standalone conditions.

Abstract: A system is described herein for controlling an inverter comprising an artificial neural network (ANN) and a space vector pulse-width modulation converter. The ANN comprises an input layer, plurality of hidden layers, an output layer, and a processor. The ANN receives a plurality of input signals and produces output signals that control a SVPWM converter. The ANN is trained to minimize a cost function and to implement optimal control based on ADP. Further, the ANN can be configured to handle rated current and PWM saturation constraints in providing volt/VAR control functions. Finally, the ANN uses averaged feedback signals instead of instantaneous feedback signals to improve volt/VAR control performance of parallel inverters. Performance evaluation shows that an ANN controller has a strong ability to maintain volt and VAR control at the grid edge and prevent fighting between inverters thereby allowing an inverter to work effectively in parallel with other ANN-controlled inverters.

Abstract: A dc/dc buck converter controller comprises an artificial neural network (ANN) controller comprising an input layer and an output layer. The input layer receives an error value of a dc/dc buck converter and an integral of the error value. The output layer produces an output error voltage. A pulse-width-modulation (PWM) sliding mode controller (SMC) is configured to receive the output error voltage and produce a control action voltage by multiplying the output error voltage with a PWM gain. A drive circuit is configured to receive the control action voltage and provide a drive voltage to an input switch of the dc/dc buck converter. A PI control block is configured to modify a reference output voltage based on a maximum current constraint input. A locking circuit is configured to maintains the output error voltage at the saturation limit of the PWM SMC to manage a maximum duty cycle constraint.

Abstract: Disclosed herein is a novel monohydrate polymorphic form of S-nitrosocaptopril (CapNO), process for preparation, pharmaceutical compositions and method of treating pulmonary hypertension, hypertension, or congestive heart failure thereof. A process for the preparation of S-nitrosocaptopril crystalline form is provided which comprises the following steps: reacting captopril, sodium nitrite and EDTA-2Na.2H2O in 15±5 wt % saline, adjusting pH to precipitate the crystal, and recrystallizing S-nitrosocaptopril monohydrate crystalline. The crystals can be stably stored at 4° C. for at least 12 months.

Abstract: Described herein is a neural network-based vector control method for the induction motor. The disclosure includes an approach to implement optimal vector control for an induction motor by using an NN; a NN controller to substitute two decoupled proportional-integral (PI) controllers in current loop; and, a mechanism to train the NN controller by using a Levenberg-Marquardt (LM)+forward accumulation through time (FATT) algorithm.

Abstract: Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

Abstract: A dc/dc buck converter controller comprises an artificial neural network (ANN) controller comprising an input layer and an output layer. The input layer receives an error value of a dc/dc buck converter and an integral of the error value. The output layer produces an output error voltage. A pulse-width-modulation (PWM) sliding mode controller (SMC) is configured to receive the output error voltage and produce a control action voltage by multiplying the output error voltage with a PWM gain. A drive circuit is configured to receive the control action voltage and provide a drive voltage to an input switch of the dc/dc buck converter. A PI control block is configured to modify a reference output voltage based on a maximum current constraint input. A locking circuit is configured to maintains the output error voltage at the saturation limit of the PWM SMC to manage a maximum duty cycle constraint.

Abstract: Described herein is a neural network-based vector control method for the induction motor. The disclosure includes an approach to implement optimal vector control for an induction motor by using an NN; a NN controller to substitute two decoupled proportional-integral (PI) controllers in current loop; and, a mechanism to train the NN controller by using a Levenberg-Marquardt (LM)+forward accumulation through time (FATT) algorithm.

Abstract: A system is described herein for controlling an inverter comprising an artificial neural network (ANN) and a space vector pulse-width modulation converter. The ANN comprises an input layer, plurality of hidden layers, an output layer, and a processor. The ANN receives a plurality of input signals and produces output signals that control a SVPWM converter. The ANN is trained to minimize a cost function and to implement optimal control based on ADP. Further, the ANN can be configured to handle rated current and PWM saturation constraints in providing volt/VAR control functions. Finally, the ANN uses averaged feedback signals instead of instantaneous feedback signals to improve volt/VAR control performance of parallel inverters. Performance evaluation shows that an ANN controller has a strong ability to maintain volt and VAR control at the grid edge and prevent fighting between inverters thereby allowing an inverter to work effectively in parallel with other ANN-controlled inverters.

Abstract: Described herein is a neural network-based vector control method for the induction motor. The disclosure includes an approach to implement optimal vector control for an induction motor by using an NN; a NN controller to substitute two decoupled proportional-integral (PI) controllers in current loop; and, a mechanism to train the NN controller by using a Levenberg-Marquardt (LM)+forward accumulation through time (FATT) algorithm.

Abstract: An example method for controlling a DC/DC converter or a standalone DC microgrid comprises an artificial neural network (ANN) based control method integrated with droop control. The ANN is trained to implement optimal control based on approximate dynamic programming. In one example, Levenberg-Marquardt (LM) algorithm is used to train the ANN, where the Jacobian matrix needed by LM algorithm is calculated via a Forward Accumulation Through Time algorithm. The ANN performance is evaluated by using power converter average and switching models. Performance evaluation shows that a well-trained ANN controller has a strong ability to maintain voltage stability of a standalone DC microgrid and manage the power sharing among the parallel distributed generation units. Even in dynamic and power converter switching environments, the ANN controller shows an ability to trace rapidly changing reference commands and tolerate system disturbances, and operate the DC/DC converter or the microgrid in standalone conditions.