231results about "Logic circuits" patented technology

An output drive circuit includes: a totem-pole output including: a high-side transistor (HST) with drain and source, an output stage power supply voltage applied to the drain, the source connected to the first node (N1); and a low-side transistor with source and drain, a ground voltage applied to the source, the drain connected to N1; and a bootstrap part including a capacitor supplying charge to a gate of HST when on, the charge being charged when HST is off, and one terminal of the bootstrap part connected to N1, the output drive circuit further including: a first transistor (T1) that conducts when HST is to be on, T1 connected between a drive circuit power supply voltage and the gate of HST; and a second transistor conducting when HST is to be turned on, the second transistor connected between the other terminal of the capacitor and HST gate.

The system described herein can automatically match, link, or otherwise associate electronic activities with one or more record objects. For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects. The system can then rank the identified candidate record objects to select one or more record objects with which to associate the electronic activity. The system can then store an association between the electronic activity and the selected one or more record objects.

The application relates to the technical field of brain-computer interfaces, in particular to a power multi-person collaborative operation authorization system and method based on a brain-computer interface. The method comprises the following steps: constructing a multi-person collaborative cognitive index system to obtain a multi-modal cognitive factor set, and constructing a value judgment matrix; a least square optimization model is established to obtain a subjective weight and perform consistency checking; objective information entropy weight is extracted in combination with historical data to obtain a comprehensive value weight and construct a fuzzy measure function; a comprehensive authorization priority evaluation value is calculated according to a real-time cognitive factor characteristic vector; disturbance analysis is carried out by using statistical distribution and Monte Carlo sampling to obtain an authorization return success rate and an authorization time delay confidence interval. The application can solve the problems of instruction transmission delay and cognitive ambiguity under strong electromagnetic interference, and through the combination of multi-person physiological cognition and edge computing power, the collaborative quality and authorization decision credibility in a high-risk power operation environment are improved.

The application relates to the technical field of electrical equipment evaluation, in particular to a multi-dimensional electrical equipment stability evaluation method and device for new energy power grid connection, which comprises the following steps: based on a multi-dimensional evaluation index system, collecting multi-dimensional index data of a target electrical equipment, performing feature enhancement processing, and generating processed enhanced features; based on a grid connection power fluctuation coefficient and an equipment operation stage coefficient of the target electrical equipment, adjusting the weight proportion of each dimension and corresponding indexes in the multi-dimensional index data to obtain dynamic weights; inputting the processed enhanced features and the dynamic weights into an evaluation model combining fuzzy comprehensive evaluation and Markov chain prediction, performing stability evaluation on the target electrical equipment for new energy power grid connection, and outputting a stability evaluation result of the target electrical equipment. Therefore, the problems that in the related art, prospective decision support cannot be provided for preventive maintenance and grid connection scheduling, and it is difficult to guarantee the safe and stable operation of the power grid under high-proportion new energy access are solved.

A scalable chiplet-based system for integrated electronic control within a vehicle comprises several central chiplets (220-223), with each central chiplet (220-223) being disposed along a substantially central axis of a substrate (200) for housing the central chiplets (220-223). Several systems on chip (230-237) perform vehicle operations of the vehicle, wherein each of the systems on chip (230-237) communicate with one of the central chiplets (220-223) through a respective die to die interconnect, and each die to die interconnect includes one or more main data paths and one or more side data paths. A chip to chip interconnect (225-229) is between each of the central chiplets (220-223) through which the central chiplets (220-223) communicate.

The application relates to a response capability grading evaluation method, device and equipment based on cluster analysis. The method performs secondary clustering on the power consumption terminal according to power consumption information and response information of the power consumption terminal under different dimensions, obtains response capability grade evaluation results of the power consumption terminal under different dimensions, comprehensively determines the target response capability grade of the power consumption terminal according to the response capability grade evaluation results of the power consumption terminal under multiple dimensions, improves the comprehensiveness of information involved in the response capability grade evaluation of the power consumption terminal on the basis of fuzzy evaluation, and further improves the reliability of the response capability grade evaluation of the power consumption terminal, so that the accuracy of the demand response capability evaluation of the power consumption terminal is improved.

A serializer circuit includes flipflops and a multiplexing circuit. A first flipflop may be configured to capture a bit of data from a first data signal during each cycle of a clock signal. A second flipflop may be configured to capture a bit of data from a second data signal during each cycle of the clock signal. The multiplexing circuit may be configured to provide an output of the first flipflop as an output of the multiplexing circuit when a delayed version of the clock signal is in a first signaling state, and to provide an output of the second flipflop as the output of the multiplexing circuit when the delayed version of the clock signal is in a second signaling state. The delayed version of the clock signal may be configured to suppress data-dependent glitches in an output signal used in subsequent stages of a receiving device.

This invention discloses a digital identification system and method for high-pressure testing instrument detection data. The system includes: a data acquisition and conversion module for acquiring and converting analog signals; a data preprocessing module for noise removal and anomaly detection; a key parameter extraction module for extracting and optimizing key parameters; a data analysis and identification module for analyzing data and issuing early warnings of anomalies; a feedback adjustment module for optimizing test parameters; and a display and control module for displaying data, analysis results, and alarm prompts. This invention combines sliding window time series analysis, Kalman filtering, temporal convolutional neural networks, and adaptive fuzzy logic control algorithms to intelligently analyze and process the detection data of high-pressure testing instruments, enabling fault early warning, precise optimization, real-time adjustment, and maintenance decision-making. The system features high precision, strong real-time response capabilities, and significant optimization and maintenance effects, significantly improving data processing capabilities and decision-making efficiency during high-pressure testing.

A divider circuit is disclosed. The divider circuit includes a pair of differential latch circuits. The divider circuit receives complementary clock signals as a pair of differential signals. The divider circuit generates frequency divided signals based on the complementary clock signals, inverted versions of the complementary clock signals, and input signals. The frequency divided signals may be substantially ripple-free. Further, the frequency divided signals remain substantially ripple-free in a presence of the skew between the complementary clock signals.

A laser-based target damage evaluation method, device and equipment, the laser-based target damage evaluation method comprising: obtaining a target damage evaluation system; calculating the target power density of laser through the target power density model of laser; predicting the response of each evaluation index of target through the damage effect proxy model based on the target power density of laser; calculating the fuzzy evaluation result vector of each evaluation index according to the response of each evaluation index and the corresponding membership function; according to the fuzzy evaluation result vector of each evaluation index and the corresponding weight, weighted fusion is carried out layer by layer to obtain the fuzzy evaluation result vector of the whole target; the comprehensive vector is obtained by normalizing the fuzzy evaluation result vector of the whole target, and the damage level corresponding to the maximum membership degree vector in the comprehensive vector is evaluated as the damage level of the target. Through the present application, the final target damage evaluation result can be more accurate.

Various techniques are provided to efficiently synchronize clock and data signals in programmable logic devices (PLDs). A method includes configuring a programmable logic device (PLD) having a fabric of programmable logic blocks arranged in a plurality of regions; routing data carry chains in a first direction across the fabric to each of the plurality of regions; placing global clock circuitry at a first edge of the PLD; and routing the global clock to a corresponding first edge of each region via a global clock trunk and a plurality of clock branches, the global clock trunk propagating the global clock signal across the fabric and in each region, in the same direction as the data carry chains.

Aspects of the disclosure are directed to variable shift frequency implementation. In accordance with one aspect, the disclosure includes synthesizing a divided clock (511) from a shift clock (501) with a divided frequency equal to a reference frequency divided by a selected integer divisor in a logical built-in self-test (LBIST) mode; transitioning the LBIST mode from a shift state to a capture state by enabling a capture control enable signal (502) while pacing with the divided clock (511); generating a stretched capture control enable signal from the capture control enable signal using a slow cycle programmer (SCP, 530) with a plurality of quad stage pipelines; and transitioning the LBIST mode from the capture state to a subsequent shift state with the stretched capture control enable signal in a HIGH state to mitigate voltage droop.

The present application relates to the technical field of intelligent decision-making assistance for financial marketing, and particularly relates to a decision-making assistance method and system for personalized scenarios. The method comprises the following steps: constructing a fuzzy complementary judgment matrix, calculating an initial weight vector based on the fuzzy complementary judgment matrix, obtaining a candidate weight vector through row coupling degree and column coupling degree constraint optimization, generating a personalized weight vector according to the vector similarity between a borrower feature vector and the cluster centers of each customer cluster, synthesizing the borrower feature vector and the personalized weight vector to obtain a decision result, performing credit compliance verification on the decision result, and outputting the decision result if the verification is passed. The present application obtains a comprehensive scenario adjustment vector by using the vector similarity between a borrower feature vector and the cluster centers of each customer cluster, and optimizes the candidate weight vector using the comprehensive scenario adjustment vector to obtain a personalized weight vector, so that personalized credit decision-making can be realized.

An encapsulation block for a digital signal processing (DSP) block. The encapsulation block includes DSP block having an input terminal, an output terminal, and an input clock. The encapsulation block also includes pacing control network operatively connected with the input terminal, the output terminal, and the input clock of the DSP block. The input terminal of the DSP block is configured to receive a samples-in data stream inputted at a predefined clock period defined by the input clock. The output terminal of the DSP block is configured to receive a samples-out data stream outputted at a predefined paced parameter. The pacing control network is configured to control data flow at the samples-in data stream and the samples-out data stream independently of the DSP block.

This invention proposes a method and system for recommending English annotation methods with low forgetting rate based on the Analytic Hierarchy Process (AHP), belonging to the fields of optimization and intelligent decision theory. First, from the perspective of English learners, this invention analyzes and identifies five key factors affecting the rate of forgetting English vocabulary. Then, using the Analytic Hierarchy Process (AHP), a weight matrix that passes consistency detection is constructed for each influencing factor, and the actual influencing factor values ​​are normalized based on fuzzy logic. Based on the weight matrix that passes consistency detection and the normalized influencing factor scores under different text annotation methods, the estimated forgetting rate of English vocabulary for English learners under the corresponding annotation methods is obtained. The annotation method with the lowest estimated forgetting rate is selected as the optimal recommended annotation method to assist English learners in vocabulary learning. Finally, a one-month experimental verification of the proposed recommendation algorithm is conducted, and the experimental results demonstrate the effectiveness of this invention.

This invention relates to a knowledge and data fusion-driven, interpretable primary headache auxiliary identification method. The method includes: collecting patient information and preprocessing it to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into an interpretable primary headache auxiliary identification model to obtain a probability distribution of headache identification results; outputting the headache type with the highest probability and its identification confidence, and generating a risk warning; wherein, the hierarchical fuzzy logic layer of the interpretable primary headache auxiliary identification model, based on the multi-dimensional feature vector, calculates the fuzzy membership degree of each feature when it belongs to different headache types according to the differentiated membership function of different headache types, obtaining fuzzy membership degree features; the KAN feature fusion network fuses the fuzzy membership degree features with the original vector to obtain classification features; the classifier outputs the probability distribution of headache types based on the classification features. Compared with existing technologies, this invention provides a solution for intelligent headache diagnosis that combines accuracy, robustness, and interpretability.

The present application belongs to the technical field of federated learning, and particularly relates to a space information network bidirectional individualized federated learning method, system and device, which comprises the following steps: firstly, aggregating pre-training models of each ground station to obtain a global initial model and broadcasting the global initial model to clients; in subsequent rounds, a satellite distributes ground station models to clients in a targeted manner; the clients aggregate multiple ground station models by weighting with historical similarity as the weight, generate an individualized initial model for training, calculate the similarity metric and membership of the client model and the ground station model, fuse the historical similarity to update the similarity metric and membership, assign a set of collaborative ground stations to the clients and forward the model, and the ground stations aggregate the client models by weighting with historical similarity as the weight to obtain updated specialized models. Through the cooperative bidirectional individualized mechanism, the present application solves the model adaptation problem caused by data heterogeneity in the space information network, ensures the stability of training, and improves the performance of the individualized models of the clients and the ground stations.

A level shifter for a power converter comprises: a differential detector; a first transistor with a drain electrically connected to a first node of the differential detector, a gate, and a source; a second transistor with a drain electrically connected to a second node of the differential detector, a gate, and a source; and a circuit configured to simultaneously apply an equal voltage to the source of the first and second transistors based on a digital signal input to the level shifter. The circuit is also configured to apply a different gate voltage, depending on the input digital signal, to select which side of the level shifter will draw current to the differential detector.The differential detector is configured to convert the digital signal into a different voltage level based on a differential current between the first and second transistors.

In a method of predicting an optimal process condition model for a semiconductor fabrication process, process parameter information of a unit process in the semiconductor fabrication process may be collected. First characteristics information of objects to be processed before the unit process and second characteristic information of processed objects after the unit process may be extracted. Process global uniformity (PGU) may be calculated using the first characteristic information and the second characteristic information. A data set of the unit process may be created using the process parameter information and the PGU. A virtual process environment function of the unit process may be created using the data set. The optimal process condition model of the unit process may be created using the virtual process environment function.

This invention proposes an innovative model predictive control-based current control method and system for permanent magnet synchronous motors (PMSMs), aiming to address the problems of slow dynamic response and low control accuracy in traditional control methods when facing parameter variations and external disturbances. By constructing an accurate mathematical model, this invention not only improves the dynamic response speed and control accuracy of the motor but also significantly reduces torque ripple, thereby enhancing the stability and reliability of motor operation. Furthermore, the introduction of intelligent algorithms such as neural networks or fuzzy logic, as well as adaptive control algorithms, further enhances the system's adaptability and robustness. The application of space vector modulation technology optimizes the current waveform, further reducing torque ripple and improving the smoothness of motor operation. The control method of this invention is suitable for industrial applications with extremely high requirements for dynamic performance and control accuracy, demonstrating its broad application potential in the field of modern industrial motor control.

An integrated circuit (240), and related method, are disclosed. The integrated circuit (240) includes a first semiconductor device (245). The first semiconductor device (245) includes a first programmable gate array (246); a plurality of interface logics (252,259) including a first interface logic (259); and a first memory port connected to a first set of bonds (258, 260) on a surface of the first semiconductor device (245). The first programmable gate array (245) is operatively coupled to the first interface logic (252) to communicate via the first memory port (251).

A receiver includes an input node coupled to receive an analog signal, a first switch coupled between the input node and a first node, a second switch coupled between the input node and a second node, a first resistive element coupled between the first node and a reference node, a second resistive element coupled between the second node and the reference node, a first capacitive element coupled to the first node, and a second capacitive element coupled to the second node. The receiver further includes a comparator having a first input coupled to the input node to receive the analog signal, and a second input coupled to the reference node to receive a reference voltage, wherein an output of the comparator controls the first and second switches.

One embodiment includes a method for training a transformer model on a resource-constrained device. The method includes encoding input tokens into hidden representations using tensor-compressed embedding tables stored in tensor-train-matrix (TTM) format. The method includes performing forward propagation on the hidden representations through tensor-compressed layers including tensor-train (TT) decomposed weight matrices to generate model outputs, wherein the forward propagation maintains all weight parameters in compressed tensor format without reconstructing dense weight matrices. The method includes computing gradients of loss from the model outputs. The method includes backpropagating the gradients through the tensor-compressed layers to compute gradients with respect to tensor cores of the TTM format embedding tables and the TT format weight matrices using tensor network contractions. The method includes updating the tensor cores of the tensor-compressed embedding tables and weight matrices directly in compressed format using the computed gradients with respect to the tensor cores.

The present invention belongs to the field of intelligent computing, and specifically relates to a multi-task collaborative attention TSK fuzzy system modeling method, which is used to provide an interpretable multi-task intelligent evaluation model for fermentation food safety assessment. The method includes two main parts: a new multi-task TSK fuzzy system model structure and a multi-task collaborative optimization process. In the intelligent evaluation model, the proposed multi-task collaborative processing unit is used to perform collaboration among multiple evaluation tasks. By using the multi-task feature selection layer and task attention structure respectively to extract the unique information of each task and the relevant information between tasks, the evaluation performance of each evaluation task can be better improved. In the collaborative optimization process, the present invention uses multi-task collaborative regularization to achieve more efficient mining and utilization of the unique information of each task.

The application belongs to the technical field of data processing, and provides a kind of engineering structure flexibility level calculation method based on fuzzy evaluation method, comprising: establishing the finite element model of target engineering structure, according to finite element model and historical earthquake record, nonlinear time history analysis is carried out on target engineering structure, and seismic vulnerability curve is established;According to the seismic vulnerability curve, determine a plurality of loss variables, assign each loss variable to its own triangular fuzzy number, and obtain the standardized fuzzy decision matrix after standardization processing;Based on the standardized fuzzy decision matrix, combined with importance judgment and fuzzy solution, the weight coefficient of each loss variable is determined;Based on the standardized fuzzy decision matrix and weight coefficient, combined with the approximation of ideal solution sorting algorithm, the structure flexibility level index is obtained.The scheme provided by the application can not only comprehensively integrate multidimensional loss variables, but also process the fuzziness and uncertainty of the whole evaluation process.

The present application relates to the technical field of context adaptation, and specifically discloses a multi-dimensional perception adaptive counter-question triggering and context adaptation method and system, comprising the following steps: step S1: performing semantic fuzzification processing on an initial creative text input by a user, extracting keywords and assigning them to a preset fuzzy set to generate an intent fuzziness vector; step S2: combining the correlation degree matrix between each fuzzy set to calculate an intent entropy value for quantifying the uncertainty of the user's intent; step S3: when the intent entropy value exceeds a threshold value, automatically triggering a challenging counter-question to guide the user to supplement key information, and splicing the feedback and the original text to form an enriched creative context; and step S4: inputting the creative context into a plurality of pre-built expert agents, each agent outputting professional challenge opinions based on a domain knowledge base; screening out effective opinions by analyzing the support degree between the challenge opinions, and performing semantic fusion with the creative context to generate an optimized creative scheme text.

Embodiments of this application relate to the field of memories, and provide a data input / output circuit and a memory. The data input / output circuit includes a parallel-to-serial conversion circuit, configured to receive N pieces of data, and serially output the N pieces of data in a first time period; a read enable signal generation circuit, configured to generate a read enable signal, the read enable signal being at an active level in the first time period; a resistance control circuit, electrically connected to the read enable signal generation circuit, and configured to receive a termination resistance control code, an output drive resistance control code, and the read enable signal, and choose, based on the read enable signal, to output the termination resistance control code or output the output drive resistance control code; and an output drive circuit, electrically connected to the parallel-to-serial conversion circuit and the resistance control circuit, and configured to receive the N pieces of data and a signal output by the resistance control circuit, configure an output drive resistance value based on the signal output by the resistance control circuit and output the N pieces of data, or configure a termination resistance value, where N is a positive integer greater than 1.

This invention relates to short-term wind speed prediction using an interval-type 2 T-S fuzzy model based on phase space reconstruction, belonging to the field of intelligent prediction and advanced control technology for new energy power systems. It solves the problems of large errors, long prediction times, and unanalyzable logic in existing wind speed prediction models. The method includes: collecting and preprocessing wind speed data up to the current moment to obtain a wind speed time series; reconstructing the phase space of the wind speed time series to obtain a phase space trajectory matrix; inputting the phase space trajectory matrix into a trained interval-type 2 T-S fuzzy model to output short-term wind speed prediction values; wherein, in the interval-type 2 T-S fuzzy model, the antecedent uses an irregular Gaussian membership function to describe the fuzzy characteristics of the input variables, and the left and right standard deviations of the irregular Gaussian membership function are adjustable. This achieves effective extraction of chaotic features from the wind speed sequence and real-time prediction of short-term wind speed.