14 results about "Relational equation" patented technology

The invention provides a cogeneration unit peak regulation capability evaluation method, which comprises the following steps: firstly, constructing a thermodynamic system mathematical model, solving system coupling parameters under a deep peak regulation working condition through a multi-layer iterative loop architecture, and generating a multi-working-condition data set which covers a full working condition and meets physical consistency; secondly, deducing a linear qualitative relation equation of the power, the main steam flow and the heat supply steam extraction flow, preprocessing a data set, training a pure data driving model as a performance reference, introducing the linear qualitative relation equation as a physical constraint item into a loss function, and constructing and training a PINN; and finally, the rated boundary condition of the peak regulation capacity is determined, a thermal characteristic curve is obtained through PINN simulation, the peak regulation capacity range and the real-time peak regulation margin under different heat supply requirements are calculated, and the result is transmitted to a power system dispatching platform. According to the method, the all-working-condition peak regulation evaluation precision and reliability can be improved, and the flexibility and universality of the model are enhanced.

The application provides a free bending forming springback amount prediction and compensation method based on axial advancing speed, characterized in that on the basis of studying the influence of axial advancing speed on the forming precision of pipe materials, a linear equation between the bending angle and the springback angle is first obtained, and then the corresponding relationship between the parameters and the axial advancing speed is established according to the corresponding parameters of the relational equation; the application realizes the bending angle error prediction and compensation function caused by the change of the axial advancing speed, can obtain a pipe material component with an error of no more than 2.5% between the experimental piece and the simulation piece, improves the forming precision of the pipe material, and realizes the precision error compensation caused by the selection of the axial advancing speed.

A method for precision forming by continuous free bending starts with establishing a correlation equation of a continuous axis f(x) to a bending radius R and determining a bending radius R at a real-time location in the axis. Based on the free bending technique, the method further involves establishing a correlation model of a real-time bending radius R of a tube to an eccentric distance U of a bending die and hence correlations of the equation of the axis to free bending parameters, and constructing a complete correlation model among f(x), R, U, and t based on a relational equation of an eccentric distance U to movement time t of the bending die to enable the precision forming of a complex component by continuous bending. Accordingly, the production efficiency can be improved.

The present application relates to the field of artificial intelligence, and discloses a risk assessment index method based on gradient leakage input of a full connection network model, comprising the following steps: S1: constructing a gradient and input relationship equation of a multi-classification multi-input task; S2: calculating the rank and condition number of the coefficient matrix under four risk levels to assess the risk of gradient leakage data; S3: starting from the first risk level, until a certain risk level meets the standard, then the risk level of the gradient leakage data of the current model is authenticated. Compared with the existing gradient leakage input index, the evaluation method proposed by the present application is efficient and more in line with the actual evaluation needs. The method provided by the present application does not depend on any pre-trained statistical model and is suitable for model training tasks of multi-classification multi-input samples which are widely used. The index proposed by the present application is accurate and has a strict theoretical basis.

There are provided a thickness measurement method, an X-ray analysis apparatus, an information processing apparatus, and a computer program that can measure the thickness of each layer of a multi-layer sample, which has been difficult to measure using conventional techniques. In the thickness measurement method, a sample including a plurality of layers is irradiated with X-rays so that the X-rays pass through the plurality of layers, fluorescent X-rays generated from the sample are detected, transmitted X-rays that have passed through the sample are detected, a first relational equation that expresses a relationship between the intensity of the detected transmitted X-rays and the thickness of each layer and a second relational equation that expresses a relationship between the theoretical intensity ratio of the fluorescent X-rays and the transmitted X-rays corresponding to the thickness of each layer and the intensity ratio of the detected fluorescent X-rays and the detected transmitted X-rays are created, and the thickness of each layer that simultaneously satisfies the first relational equation and the second relational equation is calculated.

The application provides a kind of commuting network evaluation method and system based on artificial intelligence, through S1 generate the smooth manifold of commuting network on Lie group space, according to S2 construct dynamic traffic manifold, through S3 construct multi-agent forest model, generate commuting score vector through agent forest model, then through S4 generate score relationship equation to integrate commuting score vector, and the three commuting paths of highest score are marked and pushed to the user, considering various commuting factors, so that the final commuting score is more accurate, while adding the historical data of the user, more in line with the needs of the user, through S5 obtain the final commuting path of the user and carry out tracing, while generating traceability report, according to S6 optimize dynamic traffic manifold, and return to S2, find the factors affecting the score by the method of tracing and analyze, while optimizing the dynamic traffic manifold according to the analysis result, to achieve the purpose of optimization evaluation.

The invention discloses a medium-high speed area sensorless control method of a sliding mode observer. The method comprises the following steps: generating a current estimation value; generating a current error estimate; inputting the current error estimation and the error estimation differential into a fuzzy controller, dynamically adjusting an intermediate gain according to the input current observation deviation and the differential, and obtaining a gain coefficient according to a relation equation between the system observer gain and the intermediate gain; substituting the gain coefficient and the current error estimation into a back electromotive force estimation equation to obtain a back electromotive force estimation value; residual high-frequency noise is filtered out from the back electromotive force estimated value through a low-pass filter, the back electromotive force estimated value is sent to an angle calculation module, the angle calculation module increases phase compensation for the back electromotive force estimated value, an estimated rotor electrical angle and an estimated rotor electrical angular speed are obtained, the buffeting problem is solved, and stable control is achieved.

The present invention relates to the technical field of on-orbit space mechanisms, and discloses a method for predicting the remaining life of a space mechanism, specifically as follows: 1) Determine the life index of the space mechanism and the key components affecting the life index; 2) Establish a relationship equation between the performance loss rate of the key components and the service time; 3) Substitute the performance degradation data obtained from the key component relationship equation into the digital twin model of the space mechanism to obtain a data set X of the life index data of the space mechanism at different service times; 4) Calculate the health index HI of the space mechanism at different service times according to the data set X to obtain a data set of the health index HI; 5) Train the bidirectional LSTM network model using the data set of HI; 6) Collect the real-time life index data of the space mechanism in the service state, process the real-time life index data according to step S4 to obtain the current real-time HI; input the real-time HI into the trained bidirectional LSTM network model for prediction, construct an HI prediction curve according to the prediction result, and calculate the remaining useful life RUL of the space mechanism according to the HI prediction curve.

An estimation method includes estimating, using a relational equation, a measurement variation in a group delay from one or both of a loss in a target optical fiber and a power of a target light source. The group delay occurs when light generated by the target light source is inputted into the target optical fiber.

To provide an adjustment system capable of easily adjusting drive characteristics of a solenoid proportional valve.SOLUTION: An adjustment system (1) includes a solenoid proportional valve (13) provided in a hydraulic device (10), which includes a flow path control unit (132) that controls a hydraulic pressure or a flow rate in a flow path according to a relational expression related to drive characteristics and an acquisition unit (131) that acquires a parameter of the relational expression from the outside by wireless communication. The adjustment system also includes an information processing device (20) provided separably from the hydraulic device, which receives an input of a parameter from an operator, determines a relational expression from the parameter, presents the determined relational expression to the operator, and transmits the parameter to the solenoid proportional valve by wireless communication. Further, the adjustment system includes a measurement device (12) that measures an output value of the hydraulic device or a device related to the hydraulic device and presents a measurement result to the operator.SELECTED DRAWING: Figure 1

The application provides a kind of composite continuum mechanical arm control method, system and mechanical arm, consider friction loss, analyze drive wire node tension transmission relationship, obtain drive wire tension distribution, and then equivalent flexible beam deformation drive;The statics analysis is carried out to the cut continuum mechanical arm, the static model is established using Euler-Bernoulli beam theory, and the static model is solved using Taylor series expansion method;Based on the static model data, the end trajectory of the cut continuum mechanical arm is fitted using Kepler ellipse equation, and the relationship equation of the relative end coordinates of the concentric tube continuum mechanical arm is constructed;According to matrix coordinate conversion, the equation of the key parameters required for solving control parameters is established and iteratively solved, and the control parameter equation set is constructed to solve the control parameters of the composite continuum mechanical arm.The application can improve the timeliness of control response, improve the control accuracy and precision.

The application relates to the field of artificial intelligence and discloses a federal learning gradient defense method based on label repetition rate. Through rank analysis of a relationship equation of gradient leakage data, it is found that if the same label exists in batch samples and the obtained label prediction probability is similar, the effect of the gradient leakage attack can be effectively reduced, that is, it is difficult to reconstruct the input sample from the gradient. Therefore, in the data set loading stage, the samples of the data set are placed in the corresponding label bucket according to the label category, then the samples are placed in the corresponding confidence layer according to the confidence in the label bucket, finally, the sample combination is formed according to the setting of the label repetition number, and the sample combination is shuffled to form a new data set. The federal learning gradient defense method based on the label repetition rate proves that the label repetition rate can defend against the gradient leakage attack in theory through rank analysis of the relationship equation of the gradient leakage data, and the method is verified in practical application.

The invention provides a commuting network assessment method and system based on artificial intelligence, and the method comprises the steps: generating a smooth manifold of a commuting network in a Lie group space through S1, constructing a dynamic traffic manifold according to S2, constructing a multi-agent forest model through S3, and generating a commuting score vector through the agent forest model. And S4, generating a score relation equation to integrate commuting score vectors, marking three commuting paths with the highest scores and pushing the three commuting paths to the user, considering various commuting factors to make the final commuting score more accurate, adding historical data of the user at the same time, better meeting the requirements of the user, and improving the user experience. And S5, obtaining a final commuting path of the user, performing tracing, generating a tracing report, optimizing the dynamic traffic manifold according to S6, returning to S2, searching and analyzing factors influencing scores through a tracing method, and optimizing the dynamic traffic manifold according to an analysis result, thereby achieving the purpose of optimization evaluation.

To correct speed of sound effects in mass flow rate by using mass flow rate equations and relationships that account for speed of sound.SOLUTION: A method for inferring an inferred speed of sound of a flow fluid is disclosed. The method is conducted by a computer system (200) having a processor (210) and a memory (220), the processor (210) configured to execute instructions from the memory (220) and store data in the memory (220), the memory (220) having a SoS inference module (202). The method includes inferring, by the SoS inference module (202), the inferred speed of sound of the flow fluid based on an inferential relationship between an inferred density of the flow fluid and the inferred speed of sound of the flow fluid.SELECTED DRAWING: Figure 1