Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

矩形板结构损伤的分区域神经网络识别方法

通过引入LM优化算法,针对矩形薄板中对称结构的损伤识别问题,提出了一种基于神经网络的分区域分步识别方法.对于预测输出量比较多且对预测精度要求比较高的问题,常会出现网络训练时收敛速度慢、网络预测精度低,并且当网络训练达到目标误差时,输出的预测量中常有某个输出量的误差还很大的情况.针对这些问题,利用选取的组合输入参数,提出了基于神经网络的分区域识别方法.通过对悬臂板结构的数值模拟结果表明:提出的分区域识别方法对结构损伤的分区和预测是可行和有效的,其预测精度要明显的高于只用单个网络的预测结果,并且预测子网络对损伤的位置和程度是同步输出的,从而避免了传统分步识别理论中子网络过多的问题.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

基于BP网络和遗传算法的蜗杆传动优化设计CAD系统

如何将优化方法和CAD建模过程无缝集成在一起是一个急需解决的有重大应用价值的课题。以蜗杆传动优化设计CAD系统的开发为例进行了初步探索。首先介绍了蜗杆传动优化设计的两大关键技术:基于BP网络实现图表的逼近,基于遗传算法求解混合离散变量优化问题。然后根据国家最新标准,建立蜗杆传动优化设计的规范化数学模型。最后介绍了Visual Studio 2008环境下采用Pro/Toolkit二次开发Pro/ENGINEER Wildfire 4.0软件开发蜗杆传动优化设计CAD系统的过程。

A Novel Simulation Framework for Predicting the Formation Parameters Variation in Unconsolidated Sandstone Reservoir

After long-term waterflooding in unconsolidated sandstone reservoir, the high-permeability channels are easy to evolve, which leads to a significant reduction in water flooding efficiency and a poor oilfield development effect. The current researches on the formation parameters variation are mainly based on the experiment analysis or field statistics, while lacking quantitative research of combining microcosmic and macroscopic mechanism. A network model was built after taking the detachment and entrapment mechanisms of particles in unconsolidated sandstone reservoir into consideration. Then a coupled mathematical model for the formation parameters variation was established based on the network modeling and the model of fluids flowing in porous media. The model was solved by a finite-difference method and the Gauss-Seidel iterative technique. A novel field-scale reservoir numerical simulator was written in Fortran 90 and it can be used to predict 1) the evolvement of high-permeability channels caused by particles release and migration in the long-term water flooding process, and 2) well production performances and remaining oil distribution. In addition, a series of oil field examples with inverted nine-spot pattern was made on the new numerical simulator. The results show that the high-permeability channels are more likely to develop along the main streamlines between the injection and production wells, and the formation parameters variation has an obvious influence on the remaining oil distribution.

HMM-Based Photo-Realistic Talking Face Synthesis Using Facial Expression Parameter Mapping with Deep Neural Networks

This paper proposes a technique for synthesizing a pixel-based photo-realistic talking face animation using two-step synthesis with HMMs and DNNs. We introduce facial expression parameters as an intermediate representation that has a good correspondence with both of the input contexts and the output pixel data of face images. The sequences of the facial expression parameters are modeled using context-dependent HMMs with static and dynamic features. The mapping from the expression parameters to the target pixel images are trained using DNNs. We examine the required amount of the training data for HMMs and DNNs and compare the performance of the proposed technique with the conventional PCA-based technique through objective and subjective evaluation experiments.

Determination of Optimal Manufacturing Parameters for Injection Mold by Inverse Model Basing on MANFIS

Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in production are increasingly serious. However, there are many factors affecting the yield rate of injection products such as material characteristic, mold design, and manufacturing parameters etc. involved with injection machine and the whole manufacturing process. Traditionally, these factors can only be designed and adjusted by many times of trial-and-error tests. It is not only waste of time and resource, but also lack of methodology for referring. Although there are some methods as Taguchi method or neural network etc. proposed for serving and optimizing this problem, they are still insufficient for the needs. For the reasons, a method for determining the optimal parameters by the inverse model of manufacturing platform is proposed in this paper. Through the integration of inverse model basing on MANFIS and Taguchi method, inversely, the optimal manufacturing parameters can be found by using the product requirements. The effectiveness and feasibility of this proposal is confirmed through numerical studies on a real case example.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Review of Design of Process Parameters for Squeeze Casting

Squeeze casting(SC)is an advanced net manufacturing process with many advantages for which the quality and properties of the manufactured parts depend strongly on the process parameters.Unfortunately,a universal efficient method for the determination of optimal process parameters is still unavailable.In view of the shortcomings and development needs of the current research methods for the setting of SC process parameters,by consulting and analyzing the recent research literature on SC process parameters and using the CiteSpace literature analysis software,manual reading and statistical analysis,the current state and characteristics of the research methods used for the determination of SC process parameters are summarized.The literature data show that the number of pub-lications in the literature related to the design of SC process parameters generally trends upward albeit with signifi-cant fluctuations.Analysis of the research focus shows that both“mechanical properties”and“microstructure”are the two main subjects in the studies of SC process parameters.With regard to materials,aluminum alloys have been extensively studied.Five methods have been used to obtain SC process parameters:Physical experiments,numeri-cal simulation,modeling optimization,formula calculation,and the use of empirical values.Physical experiments are the main research methods.The main methods for designing SC process parameters are divided into three categories:Fully experimental methods,optimization methods that involve modeling based on experimental data,and theoreti-cal calculation methods that involve establishing an analytical formula.The research characteristics and shortcomings of each method were analyzed.Numerical simulations and model-based optimization have become the new required methods.Considering the development needs and data-driven trends of the SC process,suggestions for the develop-ment of SC process parameter research have been proposed.

Effects of hot deformation parameters on flow stress and establishment of constitutive relationship system of BT20 titanium alloy

The hot compression experiments were performed to investigate the effects of hot deformation parameters on the flow stress of BT20(Ti-6Al-2Zr-1Mo-1V) titanium alloy. The results show that the flow stress decreases with the increment of deformation temperature and increases with the growth of strain rate. The peak stress moves toward the direction of strain reducing and the strain rate sensitivity increases with the rising deformation temperature. There is obvious deformation heating created during hot deformation under relatively higher strain rate and lower deformation temperature. The improved back propagation(BP) neural network with 3-20-16-1 architecture has been employed to establish the prediction model of flow stress using deformation degree, deformation temperature and strain rate as input variables. The predicted values obtained by BP network agree well with the measured values, the relative error is within 6.5% for the sample data and not bigger than 9% for the non-sample data, which indicates that the ANNs adopted can predict the flow stress of BT20 alloy effectively and can be used as constitutive relationship system applied to FEM simulation of plastic deformation.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS PART 3.ESTIMATION OF LIQUIDUS TEMPERATURE AND EXPERT SYSTEM

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Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions

The finite-time Mittag-Leffler synchronization is investigated for fractional-order delayed memristive neural networks(FDMNN)with parameters uncertainty and discontinuous activation functions.The relevant results are obtained under the framework of Filippov for such systems.Firstly,the novel feedback controller,which includes the discontinuous functions and time delays,is proposed to investigate such systems.Secondly,the conditions on finite-time Mittag-Leffler synchronization of FDMNN are established according to the properties of fractional-order calculus and inequality analysis technique.At the same time,the upper bound of the settling time for Mittag-Leffler synchronization is accurately estimated.In addition,by selecting the appropriate parameters of the designed controller and utilizing the comparison theorem for fractional-order systems,the global asymptotic synchronization is achieved as a corollary.Finally,a numerical example is given to indicate the correctness of the obtained conclusions.

基于频率下降率的结构损伤自适应神经网络识别

笔者探讨了动量系数和学习率自适应调整的神经网络算法及结构裂纹损伤识别特征参数的选取,提出以反映结构损伤位置和程度的频率下降率作为结构裂纹损伤识别的特征参数,利用有限元网格细化法对结构裂纹损伤进行数值模拟,获取训练样本数据,通过自适应神经网络对结构裂纹损伤问题进行识别研究。从结构裂纹损伤识别实例的结果中可以看出,采用频率下降率和自适应神经网络技术对结构裂纹进行损伤识别分析具有较高的精度和可靠性。

车载用户界面功能卡片位置和展开方式对搜索和操作绩效的影响研究

目的针对车载交互界面,探索更符合驾驶人查看和操作的功能卡片摆放位置及展开方式。方法实验采用2(功能卡片位置:卡片在左侧/卡片在下方)×2(展开方式:窗口展开/全屏展开)的被试者内实验设计,运用眼动仪对16名被试采集相关眼动追踪数据,因变量为任务完成时间、平均注视持续时间、点击次数、注视次数、总注视时间。结果发现功能卡片位置、功能卡片位置和展开方式的交互作用对驾驶人视觉搜索和操作行为均无显著影响,但展开方式则对驾驶人视觉搜索和操作行为的完成时间有显著影响(方案一和方案二:P<0.001^(***),方案三和方案四:P<0.005^(***)),全屏展开方式能够显著降低任务完成时间。结论研究结果表明,车载交互界面中的功能卡片为全屏展开时,能显著地提高用户搜索和操作效率,从而最大限度地减少认知负荷避免分心,该研究结果能够为车载交互界面设计提供参考。

Advancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach*

One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovations in the field of object detection. While Convolutional Neural Networks (CNN) have laid a solid foundation, new models such as You Only Look Once (YOLO) and Vision Transformers (ViTs) have expanded the possibilities even further by providing high accuracy and fast detection in a variety of settings. Even with these developments, integrating CNN, YOLO and ViTs, into a coherent framework still poses challenges with juggling computing demand, speed, and accuracy especially in dynamic contexts. Real-time processing in applications like surveillance and autonomous driving necessitates improvements that take use of each model type’s advantages. The goal of this work is to provide an object detection system that maximizes detection speed and accuracy while decreasing processing requirements by integrating YOLO, CNN, and ViTs. Improving real-time detection performance in changing weather and light exposure circumstances, as well as detecting small or partially obscured objects in crowded cities, are among the goals. We provide a hybrid architecture which leverages CNN for robust feature extraction, YOLO for rapid detection, and ViTs for remarkable global context capture via self-attention techniques. Using an innovative training regimen that prioritizes flexible learning rates and data augmentation procedures, the model is trained on an extensive dataset of urban settings. Compared to solo YOLO, CNN, or ViTs models, the suggested model exhibits an increase in detection accuracy. This improvement is especially noticeable in difficult situations such settings with high occlusion and low light. In addition, it attains a decrease in inference time in comparison to baseline models, allowing real-time object detection without performance loss. This work introduces a novel method of object identification that integrates CNN, YOLO and ViTs, in a synergistic way. The resultant framework extends the use of integrated deep learning models in practical applications while also setting a new standard for detection performance under a variety of conditions. Our research advances computer vision by providing a scalable and effective approach to object identification problems. Its possible uses include autonomous navigation, security, and other areas.