4K-DMDNet:diffraction model-driven network for 4K computer-generated holography

Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography(CGH).Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the training performance and generalization.The model-driven deep learning introduces the diffraction model into the neural network.It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation.However,the existing model-driven deep learning algorithms face the problem of insufficient constraints.In this study,we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation,called 4K Diffraction Model-driven Network(4K-DMDNet).The constraint of the reconstructed images in the frequency domain is strengthened.And a network structure that combines the residual method and sub-pixel convolution method is built,which effectively enhances the fitting ability of the network for inverse problems.The generalization of the 4K-DMDNet is demonstrated with binary,grayscale and 3D images.High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm,520 nm,and 638 nm.

Assessing a Model-Driven Web-Application Engineering Approach

Model-Driven Engineering (MDE) by reframing software development as the transformation of high-level models, promises lots of gains to Software Engineering in terms of productivity, quality and reusability. Although a number of empirical studies have established the reality of these gains, there are still lots of reluctances toward the adoption of MDE in practice. This resistance can be explained by several technological and social factors among which a natural scepticism toward novel approaches. In this paper we attempt to provide arguments to help alleviate this scepticism by conducting an assessment of a MDE approach. Our goal is to show that although this MDE is novel, it retains similarities with the conventional Software Engineering approach while automating aspects of it.

NDT-Suite: A Methodological Tool Solution in the Model-Driven Engineering Paradigm

Although the Model-Driven paradigm is being accepted in the research environment as a very useful and powerful option for effective software development, its real application in the enterprise context is still a challenge for software engineering. Several causes can be stacked out, but one of them can be the lack of tool support for the efficient application of this paradigm. This paper presents a set of tools, grouped in a suite named NDT-Suite, which under the Model-Driven paradigm offer a suitable solution for software development. These tools explore different options that this paradigm can improve such as, development, quality assurance or requirement treatment. Besides, this paper analyses how they are being successfully applied in the industry.

Towards a Model-Driven IEC 61131-Based Development Process in Industrial Automation

The IEC 61131-3 standard defines a model and a set of programming languages for the development of industrial automation software. It is widely accepted by industry and most of the commercial tool vendors advertise compliance with it. On the other side, Model Driven Development (MDD) has been proved as a quite successful paradigm in general-purpose computing. This was the motivation for exploiting the benefits of MDD in the industrial automation domain. With the emerging IEC 61131 specification that defines an object-oriented (OO) extension to the function block model, there will be a push to the industry to better exploit the benefits of MDD in automation systems development. This work discusses possible alternatives to integrate the current but also the emerging specification of IEC 61131 in the model driven development process of automation systems. IEC 61499, UML and SysML are considered as possible alternatives to allow the developer to work in higher layers of abstraction than the one supported by IEC 61131 and to more effectively move from requirement specifications into the implementation model of the system.

MDCHeS: Model-Driven Dynamic Composition of Heterogeneous Service

Web Service Composition provides an opportunity for enterprises to increase the ability to adapt themselves to frequent changes in users' requirements by integrating existing services. Our research has focused on proposing a framework to support dynamic composition and to use both SOAP-based and RESTful Web services simultaneously in composite services. In this paper a framework called "Model-driven Dynamic Composition of Heterogeneous Service" (MDCHeS) is introduced. It is elaborated in three different ways;each represents a particular view of the framework: data view, which consists of a Meta model and composition elements as well their relationships;process view, which introduces composition phases and used models in each phase;and component view, which shows an abstract view of the components and their interactions. In order to increase the dynamicity of MDCHeS framework, Model Driven Architecture and proxy based ideas are used.

Model-driven path planning for robotic plasma cutting of branch pipe with single Y-groove based on pipe-rotating scheme

The automatic cutting of intersecting pipes is a challenging task in manufacturing.For improved automation and accuracy,this paper proposes a model-driven path planning approach for the robotic plasma cutting of a branch pipe with a single Y-groove.Firstly,it summarizes the intersection forms and introduces a dual-pipe intersection model.Based on this model,the moving three-plane structure(a description unit of the geometric characteristics of the intersecting curve)is constructed,and a geometric model of the branch pipe with a single Y-groove is defined.Secondly,a novel mathematical model for plasma radius and taper compensation is established.Then,the compensation model and groove model are integrated by establishing movable frames.Thirdly,to prevent collisions between the plasma torch and workpiece,the torch height is planned and a branch pipe-rotating scheme is proposed.Through the established models and moving frames,the planned path description of cutting robot is provided in this novel scheme.The accuracy of the proposed method is verified by simulations and robotic cutting experiments.

锂离子电池健康状态估计及寿命预测研究进展综述

随着锂离子电池的应用越来越广泛,锂电池健康状态的精确估计和剩余寿命的实时预测对于锂电池系统的安全运行和降低运维成本具有重要意义。锂电池内部复杂的物理化学反应和外部复杂工作条件,使得实现精准的健康状态估计和寿命预测具有挑战性。该文综述近年来锂电池健康状态估计和剩余使用寿命预测方法的研究现状,分析基于物理/数学模型、数据驱动、模型法和数据驱动融合,以及多种数据驱动融合的锂电池健康状态估计方法的优缺点及适用条件,并对比分析不同数据驱动类型的锂电池寿命预测方法。指出锂电池健康状态估计及寿命预测尚存在的问题,并对未来研究方向进行展望,对完善锂电池健康状态估计和寿命预测算法理论体系、指导实际应用技术具有重要意义。

考虑灵活资源及模数驱动方法的电力系统调度方法综述

可再生能源及负荷种类的增多给电力系统运行带来更大不确定性,也给电力系统经济调度带来挑战。深入分析总结灵活资源特性并对不确定性的准确建模是评估电力系统灵活性和实现经济调度的基础。基于模型或数据驱动的调度建模方法面临诸多挑战,将模型与数据驱动方式相结合,并充分发挥二者优势是电力系统优化调度的发展方向。该文从灵活资源分类及特性、系统灵活性评估方法及优化调度的模型与数据驱动建模3个方面进行了归纳整理。首先,从电网侧、供应侧及需求侧3个方面介绍了系统中的灵活资源,并总结了其调节特性。其次,介绍了权重分配、数理统计及包络区间3种常用电力系统灵活性评价指标,并总结了不同方法的适用性。然后,总结了模型驱动或数据驱动的应用现状及其各自优缺点,并对模型数据交互驱动的研究现状进行了概述。最后,对考虑灵活资源的电力系统调度方案研究进行了展望。

集成学习框架下的车辆跟驰行为建模

为了提高复杂行驶环境下车辆跟驰行为预测精度,提出了一种集成学习框架下融合理论驱动模型和数据驱动模型的车辆跟驰行为建模方法。基于stacking集成学习框架,选择理论驱动的智能驾驶模型(IDM)、考虑车辆队列和周围行驶条件因素的数据驱动的长短时记忆(LSTM)网络和门控循环单元(GRU)网络作为跟驰行为特征的一级学习算法,选择3种线性和8种非线性回归方法作为备选二级学习算法来融合一级学习器的输出特征。通过对比使用实际车辆轨迹数据计算的模型预测精度,确定了最优模型。研究结果表明:包含车辆队列和周围行驶条件变量的数据驱动跟驰模型比IDM模型的预测精度更高;多数情况下采用非线性二级学习算法的融合跟驰模型的预测精度高于IDM模型、数据驱动跟驰模型以及采用线性二级学习算法的融合跟驰模型;分别采用GBRT回归和随机森林回归作为二级学习算法的IDM-LSTM-stacking模型和IDM-GRU-stacking模型具有最高的预测精度;外界干扰下的融合跟驰模型稳定性优于单一的理论和数据驱动跟驰模型。集成学习为驾驶行为建模提供了新方法。

基于模型驱动的密码算法可视化开发平台研究

针对密码算法开发平台普适性差、无法跨平台的问题,本文采用模型驱动实现密码算法开发的方法,设计了一种基于模型驱动的密码算法可视化开发平台,提出了一种基于模型驱动的密码算法开发的领域语言—MCL密码元语言;实现了基于模型的代码生成器和代码映射器.实验证明,该开发平台仅需要开发者拖拽图形块的操作就可以实现密码算法模型的建立,然后平台可以根据建立的密码算法模型生成不同编程环境下的代码.平台实现了C和python的代码映射器模块,密码算法模型可快速映射为C、python代码.平台有较好的实用性,开发者的密码算法实现过程简洁、高效,不同编程环境下的代码均可以通过平台自动生成,提高了密码算法实现的跨平台性.

小样本学习技术在新型电力系统中的应用与挑战

数据驱动已成为新型电力系统建设及其数字化转型的核心范式,相关算法在负荷预测、状态检修、多主体调控等多项业务中展现出优越的工程效果与应用潜力。然而,实际工程数据往往面临着样本不足、样本不平衡等问题,制约了数据驱动算法的最终效果。因此,需要借助小样本学习来应对这一挑战。文中从数据、特征、模型3个层面探究了小样本学习技术,综述并分析了相关技术在场景生成、故障诊断、电力系统暂态稳定评估等业务的应用现状,并进一步指出小样本学习技术在新型电力系统中所面临的不足与挑战。

数模联动的多特征工件加工能耗预测方法研究

在实际切削加工过程中材料去除率是不断变化的,现有将其视为恒量的能耗建模方法难以实现能耗准确预测。为了提高切削过程能耗预测精度,提出了一种基于材料去除率的数模联动加工能耗预测方法。首先,基于切削过程刀具与工件的接触关系分析了切入、完全切入和切出阶段材料去除率变化规律,并对相应的加工能耗特性进行了分析;其次,提出了数据驱动的刀具切入,切出阶段加工能耗预测方法,以及模型驱动的完全切入阶段加工能耗预测方法,实现加工过程能耗准确预测;最后,利用实验案例验证了所提模型及方法的有效性,为今后研究能耗预测精度奠定了基础。

基于弹性力学第一性原理的数据驱动力学建模

提出了一种基于弹性力学第一性原理的数据驱动力学建模方法,其能够从基于弹性力学方程的数值计算结果建立简洁且能准确捕捉变形机制的力学模型。基于有限元计算得到的高精度数据和无监督数据驱动控制方程识别方法Seq-SVF,从梁的载荷和位移数据中自动识别出了Timoshenko梁形式的弯曲控制微分方程,得到了三种不同加载条件下剪切影响系数关于结构尺寸和力学参数的函数表达式。揭示了经典模型适用的加载条件,同时还给出了一种未发现的新模型。通过将基于弹性力学的第一性原理计算与数据驱动范式相结合,克服了传统建模方法的局限性和对人类经验的强依赖性,为建立简洁的力学模型提供了一种新途径。

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

复杂系统测试性设计与故障诊断策略研究进展

测试性设计是提高系统可靠性、安全性、维修性、保障性的重要前沿技术,决定了系统故障检测率和隔离率,直接影响系统的维护(测试)成本。系统测试性设计包含结构化设计、模型化设计、数据驱动设计等多种设计策略。其中,数据驱动设计于近年逐渐兴起并成为重要发展方向之一,该类方法通过对系统测试与故障之间的关系进行建模,依据测试结果进行故障推理,形成故障诊断方案。首先,简要回顾了系统测试性设计的发展历程;其次,重点介绍了测试性设计的研究进展,分析总结了结构化、模型化、数据驱动3类测试方案;然后,介绍了测试性诊断策略构建,根据测试方案中的建模方法确定诊断策略的构建技术,并总结归纳了每类技术的研究特点和适用性;最后,探讨了当前复杂系统测试性设计面临的挑战性问题和可能的未来研究方向。

OBN地震数据成像处理基本逻辑与关键方法技术

海洋油气勘探逐渐进入深水深层勘探领域,地下地质构造复杂(横向变速剧烈)、目标油藏复杂(由以构造油气藏为主转向构造与地层岩性油气藏并重),同时还可能伴随海底地形及附近岩性的复杂变化,所有因素促使海洋油气地震勘探技术不断变革。提高海洋油气勘探效益的首要问题是发展尽可能满足高精度地震波成像需求的地震数据采集技术及对应的高精度地震波成像技术。当前,无论海上和陆上油气地震勘探,“两宽一高”地震数据采集技术和全波形反演(FWI)/最小二乘逆时偏移(LS_RTM)为代表的地震波成像技术是标志性的领先技术。海上油气地震勘探中,海底节点(OBN)地震数据采集是目前业界公认的、最有可能真正实现“两宽一高”地震数据采集的技术。与拖缆数据采集相比,OBN数据采集具有宽方位照明、数据信噪比高、无检端鬼波、存在实测的(至少一阶自由表面相关)下行波场、四分量观测等优点。尤其是宽方位照明和存在至少一阶自由表面下行波场的特点,使得OBN数据具备了对中深层复杂构造和近海底介质进行高精度成像的能力。着重讨论了高精度地震波成像对地震数据采集的要求,指出OBN数据采集在海洋油气勘探中的必要性;分析了OBN数据采集的地震波场的特点,据此提出OBN数据地震波成像处理的基本逻辑及相应的关键技术;认为海洋油气勘探中地震波成像处理的特殊问题主要由特征反射层引起,海水面、海底面和地下介质中若干强反射层构成了这些特征反射层,提出了模型驱动波动理论特征反射层相关多次波预测与压制的技术路线,并对比了几种代表性的多次波预测的基础理论;指出对应当前的线性化偏移成像算子叠前数据域与叠前成像域是等价的,据此以成像道集后处理为中心,给出期望成像道集的定义,将弱旁瓣、定量的反射系数作为保真高分辨地震波成像的目标,在两个域中尽可能完美实现地下同一反(绕/散)射点、不同炮检距反(绕/散)射子波的同相位叠加,尽可能好地实现保真高分辨带限反射系数的成像;提出最好把带限反射系数成像推进到宽带波阻抗成像的技术路线;结合OBN数据的特点,给出了OBN数据地震波成像处理的基本技术流程,指出各环节的关键方法技术。最后,针对OBN数据四分量观测的特点,指出是实际观测的多波地震波场中的波现象(主要是P_SV波)与地震波传播及模拟理论不匹配导致了当前多波成像结果达不到预期,建议重点研究实际观测的多波地震波场中的波现象与地震波传播及模拟理论不匹配的物理根源,而不是发展更高端的矢量波成像算法。期望本文的思想观点对OBN地震勘探在海洋油气勘探中的进一步应用产生积极的促进作用。

绳驱桁架结构伸展臂的主动振动控制

伸展臂由于受到空间温度变化和卫星机动动作的影响,不可避免地会产生振动。在阻尼较低的空间环境中,这种振动衰减较慢,影响伸展臂的正常工作。因此,合适的振动控制方法对于伸展臂的正常工作至关重要。本文采用拉绳作为作动器的空间桁架结构伸展臂,研究其作动绳上铰链串联顺序的优化和模型预测控制律的设计,这两部分内容均考虑了作动绳的单边约束和饱和约束。首先,通过ANSYS软件获取结构的模态信息;然后结合系统可控性原理和遗传算法计算出作动绳上铰链串联最优顺序;最后,在数值仿真中,通过设计的控制率对一个由四个单元组成的桁架结构伸展臂的振动控制过程进行仿真分析。结果表明,提出的作动绳串联铰链的方式能够有效抑制伸展臂的振动。

状态依赖型切换系统的数据驱动方法建模

切换系统是由一系列连续或离散的子系统和切换机制组合而成的一类复杂系统,状态依赖型切换系统因其复杂性而尚未被深入研究.因此,通过系统的输入输出轨迹来对状态依赖的切换系统进行数据驱动建模,利用数据挖掘技术寻找数据之间的有用信息,建立输入与输出之间更形象的表达形式;在此基础上提出一种结构框架,根据辨识轨迹的切换时刻将数据分段,借助神经网络建立子系统的模型以及切换规则,深度挖掘状态依赖切换系统的信息,得到切换系统中子系统及子系统间的信息.实验结果表明:相比传统的机理建模,本文提出的数据驱动方法将建模的复杂度降低了17.3%.

新型研究型大学的兴起与可持续发展策略

全球大学发展阶段呈现出的趋同性表明,进入21世纪第一个十年以来,大学已逐步走出“洪堡模式”,加速向“后洪堡模式”的转换。新型研究型大学的兴起是一个“全球性事件”,是基于传统研究型大学迭代升级的产物,某种意义上是一场深刻的高等教育模式革命和范式重构。新型研究型大学在我国的实践探索是新时代发展高质量高等教育,深入实施科教兴国战略、人才强国战略、创新驱动发展战略的抉择,它赋予建设世界一流大学新动能,开辟高水平研究型大学新赛道。通过对新型研究型大学的本质特征的比较分析发现,新型研究型大学之“新”突出表现为:服务国家创新驱动发展战略的定位新,推动学科交叉、加强有组织科研的学术架构新,国家、企业和社会力量办大学的多元办学主体新。作为新型高等教育机构,它在资源配置、评价体系和相关支持政策上还面临诸多困境和挑战。应进一步加强顶层设计,加速相关政策改革,制定与创新型国家建设需要相适应的高等教育布局规划,改进高校分类体系、优化高校空间布局、构建高校设置新型评价审核体系、调整学科专业结构、促进高等教育资源跨区域跨部门合理流动和资源共享以推动区域融通、产教融合和科教融汇。

支撑新型配电网数字化规划的图形⁃模型⁃数据融合关键技术

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