75 results about "Stochastic differential equation" patented technology

The invention discloses an electric power information operation violation risk supervision system based on a knowledge graph, which relates to the field of violation risk supervision and comprises a dynamic graph construction module, a causal analysis module, a strategy analysis module, a strategy modeling module and a supervision decision module. The method comprises the following steps: obtaining multi-type electric power operation field sensing data and information system data, and carrying out data processing and knowledge graph dynamic construction to obtain a dynamic knowledge graph; performing risk situation quantification and risk decision point inference based on the dynamic knowledge graph to obtain a key causal decision point set; based on the key causal decision point set, through strategy logic analysis, obtaining a logic rule set which can be directly deployed and executed; according to the method, a logic rule set which can be directly deployed and executed is subjected to dynamic strategy evolution of a stochastic differential equation to obtain a dynamic strategy model, and the dynamic strategy model is subjected to decision optimal screening of measurement transformation to obtain an optimal supervision decision set, so that a risk value can be accurately calculated, and the supervision response speed can be increased.

The invention relates to the field of transaction anti-fraud, and discloses a real-time transaction anti-fraud system based on a multi-modal behavior atlas, and the system comprises an acquisition processing module which is used for obtaining time sequence data of equipment, transaction and geographic modals and carrying out the preprocessing of the time sequence data, and obtaining the processed data; the multi-mode construction module is used for calculating the time-varying fluctuation rate of a transaction mode, the local dispersion degree of an equipment mode and the trajectory bending degree of a geographic mode, and constructing the processing data into random manifold features containing a Riemannian metric tensor; and the cross-modal interaction module is used for modeling a time-varying driving relation between random manifold characteristics based on a stochastic differential equation and a Poisson process. The method comprises the following steps: converting equipment, transaction and geographic data into random manifold features containing Riemannian metric tensor, reserving nonlinear time sequence association of multi-modal data, and describing a random drive of'equipment motion-geographic trajectory 'and a time-varying trigger relationship of'transaction-equipment activity' through a stochastic differential equation and a Poisson process.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a speech synthesis method, device, equipment and medium based on an optimization strategy algorithm, comprising: extracting a time sequence processing network unit and a data sampling scheduling unit in a speech synthesis model; mapping a denoising function in the time sequence processing network unit into a multi-step Markov decision function, and converting an ordinary differential equation in the data sampling scheduling unit into a multi-source stochastic differential equation; sampling multiple groups of independent audio tracks corresponding to the input text based on a multi-source stochastic differential equation; calculating a strategy gradient modulation factor by using an optimization strategy algorithm and a multi-step Markov decision function; optimizing strategy parameters of the speech synthesis model according to the strategy gradient modulation factor to obtain an optimized speech synthesis model; and obtaining a to-be-converted text, and synthesizing voice corresponding to the to-be-converted text by using the optimized voice synthesis model. And the speech synthesis accuracy is improved.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention discloses a unified underlying vision pre-training method based on a multi-scale diffusion model, and the method comprises the steps: constructing and training a multi-scale degradation robust variational auto-encoder (VAE) which is used for extracting the multi-scale hidden space representation of degradation robustness; training a degradation invariant image feature encoder for extracting visual semantic features irrelevant to degradation types; based on a pre-trained diffusion model backbone network, in combination with the robust hidden space representation and the visual semantic features, constructing and training a condition-controllable bottom layer visual diffusion model, and modeling a diffusion process by adopting an improved stochastic differential equation (SDE); and integrating the multi-scale degradation robust VAE with a bottom-layer visual diffusion model, and constructing a large multi-scale bottom-layer visual pre-training model. According to the method, unified pre-training of various underlying degeneration is realized, and the fidelity and robustness of image restoration are improved.

The invention relates to the field of ship route optimization, discloses a route optimization method and system based on a particle swarm optimization algorithm and geographic information, and is used for providing comprehensive decision support covering route planning, energy scheduling, risk control and carbon emission management for shipping enterprises. Comprising the steps that a commercial decision basic data set integrating geographic information, ship multi-energy system configuration and marine environment forecast data is constructed, and a commercial fractional order parameter set is generated; environment uncertainty is modeled by using a stochastic differential equation, a multi-energy system operation cost function is integrated, and a commercial cost prediction model capable of outputting operation cost probability distribution is formed; and carrying out risk-aware business strategy optimization based on a particle swarm optimization algorithm, and finally generating an executable business decision proposal containing a detailed route trajectory, a speed plan and a multi-energy equipment scheduling strategy. According to the invention, collaborative optimization of route planning and energy management is realized, and economy, safety and environmental protection of ship operation are significantly improved.

In some aspects, the present disclosure provides a method for generating a geometrical structure of a binding complex formed between a protein and a ligand. In some embodiments, the method comprises sampling an initial geometrical structure of the binding complex from a geometry prior. In some embodiments, the method comprises denoising, using a machine-learned stochastic differential equation (SDE), the initial geometrical structure to generate the geometrical structure of the binding complex.

The invention discloses a multi-scene risk perception method based on a knowledge graph and deep learning, and the method comprises the following steps: collecting multi-source heterogeneous data, carrying out the unified standardization of the multi-source heterogeneous data into a multi-modal data vector, converting the data vector into polar coordinate representation with direction and amplitude characteristics, and carrying out the inter-modal fusion through a polar coordinate transformation network; the fusion result is matched with a preset risk knowledge graph, and a dynamic graph structure containing risk nodes and edge weights is constructed; by introducing a reverse stochastic differential equation, the state of risk evolution along with time is simulated; and analyzing the risk density distribution in different directions, generating a dynamic vector for controlling risk reasoning, dynamically activating a risk channel in an asynchronous mode, and comprehensively outputting a unified multi-scene risk perception map. According to the invention, continuous and interpretable identification and prediction of the risk state in a complex environment are realized.

The invention provides a hockey team tactical position early warning and correction method and system fusing spatio-temporal features, and relates to the technical field of hockey tactical analysis, and the method comprises the steps: generating tactical feature vectors through employing a self-adaptive diffusion probability graph algorithm, and comparing the tactical feature vectors with a standard tactical library to generate an early warning; calculating position deviation according to tactical rules; a stochastic differential equation is constructed, a plurality of groups of candidate trajectories are generated, and the trajectory with the minimum energy value is selected as the correction path, so that real-time early warning and scientific correction of the hockey tactics are realized, and the tactical execution accuracy is improved.

The invention discloses a refrigerator life prediction method based on an adaptive physical information recurrent neural network, and the method comprises the steps: extracting multi-source statistical health features through a steady-state control window, capturing time sequence dependence through a recurrent neural network, and generating a degeneration state estimator; embedding the estimator into a stochastic differential equation driven by a Wiener stochastic process to enable a drift term to reflect a deterministic degradation mechanism and a diffusion term to quantify the uncertainty of a working condition; based on data deviation, equation residual and monotonicity violation, multi-constraint joint loss is constructed, Bayesian uncertainty estimation is adopted to dynamically optimize each loss weight, and adaptive balance of physical consistency and observation fitting is realized. According to the method, residual life prediction with probability distribution is output, and the precision and robustness of refrigerator health management under complex working conditions are effectively improved.

The invention relates to the technical field of carbon emission prediction and early warning, and provides a multi-scale neural distribution prediction and hierarchical migration early warning method, which comprises the following steps: acquiring historical carbon emission data, respectively inputting a historical sequence and a to-be-predicted sequence into an energy consumption stochastic differential equation model and a carbon factor stochastic differential equation model, generating a multi-scale carbon emission path sample set through an independent random disturbance term; calculating a path-level suitability score of each path sample based on the standard-exceeding risk integral, the first standard-exceeding moment and the path fluctuation variance; layering the calibration data set into a plurality of working condition layers according to working condition labels, sharing distribution shape parameters among the working condition layers through a hierarchical Bayesian method, and regularizing quantiles of small sample working condition layers to obtain an early warning threshold value of each working condition layer; and selecting a corresponding early warning threshold value according to the current working condition label to compare and trigger early warning. According to the method, the accuracy of carbon emission distribution prediction and the robustness of an early warning system are improved, and the problem that the early warning threshold value is unstable under the small sample working condition is relieved.

The embodiment of the disclosure provides a controllable palmprint sample generation method based on a stochastic differential equation; it relates to the field of computer vision. The method comprises constructing an unconditional diffusion model network architecture and defining a forward process and a reverse process; training an unconditional diffusion model according to the forward process and a palmprint image to obtain a noise image; according to the reverse process, the trained unconditional diffusion model is used for sampling the noise image to generate an unconditional palmprint sample; a conditional encoder is introduced into the trained unconditional diffusion model; the weights of the unconditional diffusion model are frozen, and the conditional encoder is trained according to a conditional image; when the conditional encoder loss function converges, the frozen weights are unlocked, and the unconditional diffusion model introduced with the conditional encoder is trained according to the forward process, the palmprint image and the conditional image; according to the reverse process, the trained unconditional diffusion model with the conditional encoder is used for sampling to generate a conditional palmprint sample. Thus, the palmprint sample authenticity, diversity and controllability are improved.

The invention discloses a random stability analysis method for an electromechanical composite transmission system, and belongs to the technical field of electromechanical transmission. The method comprises the following steps: acquiring operation data of the electromechanical composite transmission system, and estimating drift term parameters and diffusion term parameters of an Irat stochastic differential equation representing a random road load by adopting a maximum likelihood method; establishing a random state space model according to the current balance relationship, and judging the random stability of the model by adopting a moment stability analysis method to obtain a system stability analysis result; and performing numerical simulation according to the random state space model, verifying the accuracy of the system stability analysis result, and outputting a random stability verification conclusion. According to the method, random excitation can be processed by estimating the Italian process parameters based on the maximum likelihood method and adopting the moment stability analysis method, and the method has the advantages of being good in adaptability, high in calculation efficiency and high in stability.

The present application relates to the field of intelligent medical treatment, in particular to a tumor treatment dosing regimen Pareto optimization method based on forward-backward stochastic differential equation. The present application unifies the terminal tumor load, dosing cost and treatment risk into the same stochastic control framework, can establish the Pareto optimality condition corresponding to the nonlinear model, and further converts into the implementable closed-loop state feedback control law in the LQ case. Through the combination with the numerical example and the Pareto frontier analysis, an implementable, interpretable and scalable technical path is provided for the multi-objective individualized treatment regimen design, which can solve the problem that the existing technology is difficult to uniformly process the terminal therapeutic effect, dosing consumption and cumulative risk, and difficult to obtain the implementable closed-loop feedback strategy under the forward-backward stochastic control framework.

This invention relates to an online prediction method for trajectory tracking success rate of modular unmanned surface vessels (USVs). The method includes the following steps: S1: Establishing a relative motion model between the USV and the desired trajectory point using stochastic differential equations; S2: Dividing the feasible state space of the USV into multiple sets, and rasterizing the spatial regions within each set; S3: Calculating the Markov transition probability of any grid within the feasible state space; S4: Recursively calculating the trajectory tracking success rate of the USV using backpropagation. This invention introduces a spatial correlation function to describe the influence of wind, wave, and current disturbances on the relative pose of the USV and the desired trajectory through a stochastic relative motion model. Simultaneously, it utilizes Markov stochastic approximation theory to predict the trajectory tracking success rate online, significantly improving the accuracy of trajectory tracking success rate prediction compared to existing technologies.

This invention discloses a visual reconstruction method and system for remote sensing interpretation based on a generative diffusion model. The method includes: acquiring high-resolution and low-resolution remote sensing image data; adding different levels of Gaussian noise to the training data using a forward stochastic differential equation until pure Gaussian noise data is obtained; training a noise conditional scoring network to predict the scores corresponding to these noisy data; adding noise to the low-resolution image using a forward stochastic differential equation to finally obtain pure Gaussian noise; using a trained neural network to guide the random noise to gradually converge and generate a super-resolution remote sensing image; rapidly identifying land cover types on the generated remote sensing image; and delineating land cover patches on the original remote sensing image and assigning patch information based on the identified land cover categories. This invention achieves a super-resolution effect from low resolution without changing the land cover types and patch boundaries, thereby reducing interpretation costs and improving interpretation efficiency.

The invention discloses an investment fund distribution method based on value network updating and strategy network updating, and the method comprises the steps: obtaining market data of a plurality of financial assets, constructing the processed market data into a high-dimensional state vector representing a market environment, and enabling the state vector to obey a controlled stochastic differential equation, the equation depends on a current moment, a state vector and an asset weight distribution action, and the asset weight distribution action is generated by a strategy network; the total expected revenue which can be obtained in the future is modeled as an entropy regularization value function, the entropy regularization value function comprises an entropy regularization instantaneous return rate, and the entropy regularization instantaneous return rate comprises an external reward and an internal reward for encouraging exploration; the entropy regularization value function is updated and optimized through a weak yoke confrontation value network and a yoke residual strategy network, a strategy network used for asset configuration is obtained, then an asset weight distribution action is generated, and a transaction instruction is generated based on the action and sent to an automatic transaction execution system.

The invention relates to a trajectory tracking success rate online prediction method for a modular unmanned ship. The method comprises the following steps: S1, establishing a relative motion model of the unmanned ship and an expected trajectory point by adopting a stochastic differential equation; s2, dividing the feasible state space of the unmanned ship into a plurality of sets, and performing rasterization processing on the space region in each set; s3, calculating the Markov transition probability of any grid in the feasible state space; and S4, recursively calculating the trajectory tracking success rate of the unmanned ship by adopting a back propagation method. According to the method, a space correlation function is introduced through a relative motion random model of the unmanned ship and an expected trajectory to describe the influence of wind wave flow disturbance on the relative poses of the unmanned ship and the expected trajectory, and meanwhile, the trajectory tracking success rate is predicted online by using the Markov random approximation theory; compared with the prior art, the accuracy of predicting the trajectory tracking success rate of the unmanned ship is remarkably improved.

The invention provides a composite degraded image fusion method, system and device and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: mapping a multi-modal image to a potential space through a pre-trained automatic encoder; performing information recovery in the potential space by adopting a potential diffusion framework based on a mean regression stochastic differential equation to obtain clean potential features of the multi-modal image after degeneration is removed; and based on the clean potential features, performing feature fusion through a potential diffusion fusion module of a U-net architecture, and according to a feature fusion result, decoding to obtain a fused image. According to the method, the applicability of image fusion in an extreme composite degradation scene is effectively improved, on the basis, preference modulation of a specific target object in the image can be achieved according to a language instruction input by a user, and various visual and semantic requirements of the user are flexibly met.

The application provides a kind of based on knowledge and data fusion driven aero-engine life prediction method, belong to aero-engine life prediction technical field;The technical problem to be solved is to provide a kind of based on knowledge and data fusion driven aero-engine life prediction method;The technical scheme for solving the technical problem is that the overall architecture of encoder-decoder is used, the encoder extracts high-level feature representation from past observations, and the decoder summarizes past information and continuously optimizes the prediction result;A GRU-SDE module is designed in the encoder, and a stochastic differential equation SDE is used to simulate the engine degradation process, which more accurately simulates the uncertainty and randomness of degradation while extracting timing dependence;A knowledge graph is constructed for the inherent knowledge of the engine to obtain a knowledge-based multi-sensor relationship matrix;The data-based relationship matrix is randomly initialized, and the two are adaptively fused during the training process;The application is applied to aero-engine life prediction.

The present invention relates to a defogging method based on the combination of stochastic differential equations and Gaussian pyramids. High-resolution and low-quality foggy images are collected and input into a Gaussian pyramid network for decomposition to generate a first multi-layer feature map and a second multi-layer feature map. The first multi-layer feature map is subjected to a diffusion process to generate a noise feature map. The second multi-layer feature map and the noise feature map are input into a NAFNet network for training to predict noise. The noise feature map is input into the NAFNet network and, through iterative processing, an initial state noise-free feature map is generated. The initial state noise-free feature map is input into a Gaussian pyramid network and, through layer-by-layer reconstruction, a defogging image is obtained. The present invention relates to a defogging method based on the combination of stochastic differential equations and Gaussian pyramids. High-resolution and low-quality foggy images are input into the Gaussian pyramid, and Fourier transform is introduced into the noise prediction network NAFNet. A loss function is introduced to optimize model parameters to improve the defogging effect of the image.

The present invention belongs to the field of bioinformatics technology, and in particular to a drug design model based on a structure-integrated Bayesian flow network and a diffusion model. The drug design model integrates the advantages of the Bayesian flow network and the diffusion model through stochastic differential equations. Atomic coordinates are sampled by stochastic differential equations, and the Bayesian flow samples the atomic type, thereby shortening the sampling time and improving the properties of the molecule. In addition, a continuous-time loss function with an analytical solution is introduced to improve the stability of the stochastic differential equation by achieving a smoother transition to the target distribution. By comparing with other models on the commonly used data set CrossDocked2020 in this field, the drug design model has achieved good results in generating the binding affinity, drug-likeness index and conformational stability of molecules, and the sampling efficiency has been improved by 25%.

The invention discloses a brain simulation-oriented high-performance numerical differential solver method and system. The method comprises the following steps of: receiving a neuron cluster model and simulation parameters thereof; the differential equation definition of the model is analyzed, and when the input is in a character string form, the input is converted into a function form; setting a current moment t, a simulation step length dt and simulation time simt; the simulation parameters are analyzed and classified; judging whether the model contains noise or not, if yes, initializing a stochastic differential equation solver, and otherwise, initializing an ordinary differential equation solver; adaptive operators of different neuron models are called in each step length to calculate numerical solutions, and variables are subjected to parallel calculation and updating in the calculation process; updating the variable yn and the current moment t when tlt; and when t is greater than or equal to simt, entering the next step of length calculation until t is greater than or equal to simt. According to the method, the high-performance differential solver supporting parallel solving of the whole neuron cluster is provided, the parallel acceleration characteristic of hardware such as a GPU is fully utilized, the simulation speed is increased, and large-scale brain simulation is achieved.

The invention discloses a stochastic differential equation-based building method for connecting a physical rendering and diffusion model, which is characterized by comprising the following steps of: on the basis of a central limit theorem, converting discrete Monte Carlo integral into continuous time SDE expression; the method comprises the following steps: constructing a Monte Carlo stochastic differential equation, establishing mapping alignment of MC-SDE variance time tau and diffusion model time step t through uniform hemisphere sampling of a unified noise source and separation of diffuse reflection and specular reflection components, pointing out a denoising earlier stage corresponding to a low sampling path tracking image, and the like. Experiments prove that the method can apply physical control to a diffusion model generation result, the low-sampling path tracking image is input into the pre-training diffusion model to complete denoising, and a high-quality rendering result is generated. Compared with the prior art, the method has the advantages that the physical control capability of the PBR and the generation flexibility of the diffusion model are effectively combined, and the technical problems that the physical controllability of the diffusion model is insufficient, the physical rendering prompt driving flexibility is insufficient, and the two are difficult to fuse are solved.

The invention relates to a multi-target model prediction control method for a wind power access multi-terminal flexible direct current system. Comprising the following steps: constructing a grey box stochastic differential equation model based on wind power randomness and multi-terminal flexible direct current system operation data, and applying Lamperti transformation to the model to obtain an equivalent state space prediction model; establishing a multi-target optimization model considering wind power real-time tracking, DC voltage stabilization and multi-converter station collaborative power distribution in the prediction domain; setting a random stability constraint by taking moment stability as a criterion, and determining the probability constraint of a system state quantity and a control quantity into an equivalent deterministic constraint; and solving the multi-objective optimization model at each sampling moment based on the random stability constraint and the deterministic constraint, obtaining the optimal control quantity of the multiple converter stations, and applying and executing the optimal control quantity. Robust stability, real-time power tracking and multi-station cooperative distribution can be achieved under the conditions of wind power random disturbance and power electronic nonlinearity, and high control precision and real-time performance are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical science and technology, and discloses a speech synthesis method and device based on direct preference optimization, equipment and a medium. An ordinary differential equation sampling process is converted into an equivalent stochastic differential equation sampling process containing random noise introduced in each integral step, and a randomized model is obtained; obtaining an input text and a corresponding reference voice, and inputting the randomization model to output a plurality of candidate voice samples; grading and screening out preference samples and non-preference samples through a preset grading strategy, and constructing a preference data pair; based on a direct preference optimization algorithm, training the two to-be-trained randomized models by using the preference data pair to obtain a preference model and a non-preference model; and finally, receiving a to-be-synthesized text, synthesizing output results through the two models, and fusing to obtain a target voice. And model optimization is guided through preference data, so that the speech synthesis quality is remarkably improved.

The invention discloses a watershed runoff simulation method fusing a random Xinanjiang model and machine learning, and belongs to the technical field of watershed runoff simulation. The method comprises the following steps: collecting hydro-meteorological data in a research basin; constructing a stochastic three-water-source Xinanjiang model based on a stochastic differential equation; utilizing Monte Carlo simulation to generate a runoff probability trajectory and extracting statistical characteristics; extracting a multi-scale feature vector of the runoff sequence based on discrete wavelet transform; and constructing machine learning models with different architectures, inputting a mean trajectory or a full feature set subjected to wavelet decomposition, and simulating the drainage basin runoff by adopting different information combinations. According to the method, the three-water-source Xinanjiang model based on the stochastic differential equation is constructed, and noise reduction and multi-scale decomposition of the runoff random trajectory are realized in combination with Monte Carlo simulation and discrete wavelet transform, so that a machine learning model can more accurately capture multi-scale hydrological signals; and the runoff simulation effect of the model is improved by improving the low and high flow simulation precision of the drainage basin.

This invention relates to the field of edge computing technology, specifically disclosing a multi-task optimization scheduling method and system based on edge computing. The method involves: acquiring the original observation reward sequence; calculating its Hearst exponent and setting the state space of a stochastic differential equation, outputting the prior distribution of the implicit true reward; updating the posterior distribution through particle filtering, extracting the posterior mean and posterior variance, and calculating the effective diversity index; training a deep reinforcement learning strategy using the posterior mean instead of the original reward, and calculating the exploration adjustment factor by combining the posterior variance and the effective diversity index; periodically reestimating the parameters of the stochastic differential equation and feeding them back to the state space. This invention can stabilize the scheduling strategy under sparse and noisy reward conditions, achieving an adaptive balance between exploration and utilization.

The invention belongs to the technical field of stability analysis of a permanent magnet synchronous generator system, and aims to solve the problems that a current wind power system cannot avoid noise generation under complex operation conditions, but the research on the stability of the system is less for the noise in the PMSG system. According to the method for researching the random stability and the random bifurcation behavior of the permanent magnet synchronous generator system under the excitation of the color noise, disturbance of random factors such as airflow fluctuation on mechanical torque of the permanent magnet synchronous generator system is described by introducing the color noise, and then the permanent magnet synchronous generator system is established; an Itstochastic differential equation of the system is obtained through a color noise unified approximation principle, a center manifold theory, polar coordinate transformation and a random average method; the random stability and the random bifurcation behavior of the system are discussed according to a maximum Lyapunov index and a probability density function method. A numerical simulation result further verifies the effectiveness and reliability of the research method.

The invention belongs to the technical field of image processing, and particularly discloses a multi-modal medical image segmentation method based on a sparse hypergraph diffusion network, and the method comprises the steps: inputting a registered multi-modal medical image into a hierarchical encoder, and extracting multi-scale features to construct a context feature pyramid; under the condition of the pyramid, a sparse hypergraph diffusion network drives a reverse stochastic differential equation to perform iterative denoising to generate a target segmentation mask, and each stage of a decoder executes prediction, quantization, gating, correction, dynamic identification and fine processing of difficult pixels; and finally, solving an equation by adopting a predictor and a corrector, converting a denoising result into a segmentation probability graph, and carrying out post-processing to obtain a final segmentation label. The multi-modal information fusion effect and segmentation precision are improved, the method is suitable for segmentation of multi-modal medical images of brain tumors, liver tumors and the like, and reliable support is provided for clinical diagnosis.