Analysis of prompt fission neutron spectrum and multiplicity for ^(237)Np(n, f) in the frame of multi-modal Los Alamos model

The improved version of Los Alamos model with the multi-modal fission approach is used to analyse the prompt fission neutron spectrum and multiplicity for the neutron-induced fission of 237Np. The spectra of neutrons emitted from fragments for the three most dominant fission modes （standard Ⅰ, standard Ⅱ and superlong） are calculated separately and the total spectrum is synthesized. The multi-modal parameters contained in the spectrum model are determined on the basis of experimental data of fission fragment mass distributions. The calculated total prompt fission neutron spectrum and multiplicity are better agreement with the experimental data than those obtained from the conventional treatment of the Los Alamos model.

Calculation of Prompt Fission Neutron from ^(233)U(n, f) Reaction byMulti-Modal Los Alamos Model

An attempt is made to improve the evaluation of the prompt fission neutron emis- sion from 233U（n, f） reaction for incident neutron energies below 6 MeV. The multi-modal fission approach is applied to the improved version of Los Alamos model and the point by point model. The prompt fission neutron spectra and the prompt fission neutron as a function of fragment mass （usually named ＂sawtooth＂ data） v（A） are calculated independently for the three most dominant fission modes （standard I, standard II and superlong）, and the total spectra and v（A） are syn- thesized. The multi-modal parameters are determined on the basis of experimental data of fission fragment mass distributions. The present calculation results can describe the experimental data very well, and the proposed treatment is thus a useful tool for prompt fission neutron emission prediction.

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Empirical Likelihood Diagnosis of Modal Linear Regression Models

In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.

Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Dynamical Model Updating Based on Modal Tests with Changed Structure

A new approach to modifying the stiffness and mass matrices of finite element models is presented to improve the calculation precision.By measuring the mode frequencies and shapes of both of the original and the new structures with changed stiffness and mass,the stiffness and mass matrices of the finite element model can be updated through matrices calculation and solving algebra equations.Taking a multi-freedom model as an example,the relation between the number of the modes and the correction precision of stiffness and mass matrix elements is researched.The facility and precision of the method are totally confirmed especially when the modeling error is known limited to a definite local range.The feasibility of the approach is proven by an effective engineering application to the model updating of a wing piece used in flutter test.

Effect of Tire Repeated Root Modal on Tire Modelling with Experimental Modal

The effect of tire repeated root modal（RRM）on tire modeling with an experimental modal is studied.Firstly,a radial tire with radial and tangential RRMs is tested and analyzed.By multi-point exciting of the radial tire,a multiple reference frequency domain method based on a least squares（LMS PolyMAX）algorithm is used to identify modal parameters.Then,modal stability diagram（MSD）,modal indication function（MIF）and modal assurance criteria（Auto-MAC）matrix are utilized to induce multiple inputs multiple outputs（MIMO）frequency response function（FRF）matrixes.The tests reveal that notable repeated roots exist in both radial and tangential response modes.Their modal frequencies and damping factors are approximately the same,the amplitudes of modal vectors are in the same order of magnitude,and the mode shapes are orthogonal.Based on the works mentioned,the method of trigonometric series modal shapes fitting is adopted,the effects of RRM model on tire modeling with a vertical experimental modal are discussed.The final results show that the effects of considering the RRM shapes are equivalent to the tire mode shapes depended on rotating the tire’s different exciting points during tire modeling,and since considering the RRM,the tire mode shapes can be unified and fixed during tire modeling.

A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION

The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.

A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION

The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.

空间大型桁架结构模型试验与动力相似研究

静力学和模态测试是空间桁架结构设计中的一个重要环节。由于空间桁架结构尺寸较大,进行全尺寸原型试验受到试验场地和设备等因素的限制。因此,空间大型桁架结构缩比模型试验不仅可以降低试验成本和风险,而且可以对空间桁架结构的静力学和振动特性分析提供新的手段。以空间大型桁架结构为研究对象,基于相似理论,运用方程分析法和量纲分析法推导了空间桁架原型和1/3缩比模型之间的静力学相似关系和模态响应的相似关系。最后,对原型桁架和1/3缩比模型桁架进行静力学和模态仿真分析,并进行了模态试验,验证模态相似关系的正确性。结果表明,通过相似理论与模型试验预测大型结构体原型的静力学和振动特性的可行性。

基于预训练语言模型的知识图谱研究综述

大语言模型时代,知识图谱作为一种结构化的知识表示方式,在提升人工智能的可靠性、安全性和可解释性方面发挥着不可替代的作用,具有重要的研究价值和实际应用前景。近年来,凭借在语义理解和上下文学习方面的优越性能,预训练语言模型已经成为了知识图谱研究的主要手段。系统梳理了基于预训练语言模型的知识图谱研究的相关工作,包括知识图谱构建、表示学习、推理、问答等,介绍了相关模型和方法的核心思路,并依据技术路径建立了分类体系,对不同类型方法的优缺点进行了对比分析。此外,对预训练语言模型在事件知识图谱和多模态知识图谱这两种新型知识图谱中的应用现状进行了综述。最后,总结了当前基于预训练语言模型的知识图谱研究面临的挑战,展望了未来的研究方向。

基于MD-BERT-LGBM的智能诊疗与预测模型设计

针对目前医疗资源紧张且难以实现精准预测病情发展的问题,文中提出了一种基于MD-BERT-LGBM的智能诊疗与预测模型。该模型引入了多模态高维度向量来表征信息,以此提高模型精确度。采用双向编码BERT替代传统的CNN算法来处理文本信息数据,同时在每个BERT模块内融合了KMP识别算法。通过归一化指数函数来预训练和校准BERT模型,用LGBM机器学习算法高效地处理表征多模态高维度向量数据。多组实验测试结果表明,与同类的CNN-MDRP模型相比,所提模型的诊疗与预测精确度分别提升了1.1%和4.0%,具有良好的可靠性。

Fault diagnosis using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

Biological Jumping Mechanism Analysis and Modeling for Frog Robot

This paper presents a mechanical model of jumping robot based on the biological mechanism analysis of frog. By biological observation and kinematic analysis the frog jump is divided into take-offphase, aerial phase and landing phase. We find the similar trajectories of hindlimb joints during jump, the important effect of foot during take-off and the role of forelimb in supporting the body. Based on the observation, the frog jump is simplified and a mechanical model is put forward. The robot leg is represented by a 4-bar spring/linkage mechanism model, which has three Degrees of Freedom （DOF） at hip joint and one DOF （passive） at tarsometatarsal joint on the foot. The shoulder and elbow joints each has one DOF for the balancing function of arm. The ground reaction force of the model is analyzed and compared with that of frog during take-off. The results show that the model has the same advantages of low likelihood of premature lift-off and high efficiency as the frog. Analysis results and the model can be employed to develop and control a robot capable of mimicking the jumping behavior of frog.

A new spatial coherence model and analytical coeffi cients for multi-support response spectrum combination

In this paper, a new spatial coherence model of seismic ground motions is proposed by a fitting procedure. The analytical expressions of modal combination （correlation） coefficients of structural response are developed for multi-support seismic excitations. The coefficients from both the numerical integration and analytical solutions are compared to verify the accuracy of the solutions. It is shown that the analytical expressions of numerical modal combination coefficients are of high accuracy. The results of random responses of an example bridge show that the analytical modal combination coefficients developed in this paper are accurate enough to meet the requirements needed in practice. In addition, the computational efficiency of the analytical solutions of the modal combination coefficients is demonstrated by the response computation of the example bridge. It is found that the time required for the structural response analysis by using the analytical modal combination coefficients is less than 1/20 of that using numerical integral methods.

Modeling and Dynamic Analysis for the Foundation of a Blade-Rotor System

In order to achieve the model-based fault monitoring and diagnosis,an accurate model for the rotor system is necessary to locate and quantify faults.Since the dynamic characteristics of a blade-rotor system is influenced by foundation flexibility,the modeling and dynamic analyses on the foundation were sequentially investigated.Firstly,the effect of element size on the model convergence was investigated using the forward difference quotient as the slope of the frequency difference,which found that the model converged when the element size refined to 4mm.Secondly,a modal analysis and a harmonic response analysis were performed to obtain the dynamic characteristics of the foundation structure.Finally,an optimization to the foundation utilizing an additional stiffener was conducted to reduce the foundation response and make the critical speed far away from the working frequency band of 20—50Hz.

卫生领域人工智能的伦理与治理:多模态大模型指南

2024年,世界卫生组织发布了“Ethics and governance of artificial intelligence for health.Guidance on large multi-modal models”,将其翻译成中文《卫生领域人工智能的伦理与治理:多模态大模型指南》供中国的同仁参阅,协助规划与卫生领域多模态大模型有关的益处和挑战,并为适当开发、提供和使用多模态大模型提供政策和实践方面的指导。世界卫生组织咨询了20位人工智能领域的顶尖专家,他们确定了在卫生领域使用人工智能的潜在益处和潜在风险,并发布了以协商方式达成一致的六项原则,供正在使用人工智能的政府、开发者和提供者在制定政策和实践时考虑。指南提供了与指导原则相一致的企业内部、政府和国际合作的治理建议,指南的基础是考虑到人类使用卫生领域生成式人工智能独特方式的指导原则和治理建议。生成式人工智能是算法在可用于生成新内容的数据集上进行训练的一种人工智能技术。指南针对其中一种类型的生成式人工智能,即多模态大模型,这种模型可以接受一种或多种类型的数据输入,并产生不局限于输入算法的数据类型的多种输出。据预测,多模态大模型将广泛应用于医疗保健、科学研究、公共卫生和药物开发等领域。多模态大模型也被称为“通用基础模型”,尽管尚未证实多模态大模型能否完成各种任务和目的。

新型电力系统中的大模型驱动技术:现状、机遇与挑战

建设新型电力系统是实现社会、经济高质量发展的重要基石,以新一代通用人工智能为主导的信息化技术与能源电力科学深度耦合,为新型电力系统的数字化转型工作提供了重要保障。为了探究电力系统在大模型时代潮流下的发展方向,该文首先系统梳理了当前电力系统的数智化发展现状以及在新的场景需求下遇到的难点问题。然后重点探讨了以多模态大模型为代表的具备深度场景解析和语言描述能力的大模型技术在电力系统中的应用前景,并分析了其在几个典型场景下的相关应用成果,证明大模型技术可行性的同时,进一步总结了大模型技术在相关电力场景所面临的机遇与挑战性问题。最后对未来大模型技术如何与电力系统实现紧密融合做了展望与建议。该研究成果有望为新型电力系统数字化转型过程中的数智化发展提供参考,助力能源电力领域提质增效。

Diversity Sampling Based Kernel Density Estimation for Background Modeling

A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.