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。

大数据驱动下的国家矿产资源管理:赋能机理与模式创新

传统矿产资源管理模式面临着国内国际复杂形势和资源安全上升国家战略地位的双重压力。大数据作为信息时代的基础资源,为国家治理现代化提供了技术支撑。探讨大数据驱动下国家矿产资源管理新模式不仅是解决当下管理困境的应有之义,更是在新时代全球资源竞合中占据优势的主动之举。基于“需求锚定—结构赋能—平台接榫”三维分析框架,界定目前我国矿产资源管理中存在的职能、体制、决策、监管四个维度的现实问题;在此基础上,针对性地分析大数据技术从虚拟治理空间、多源数据聚合、数据情报智慧、全程动态留痕四个维度赋能国家矿产资源管理的机理;围绕大数据平台支撑下的职责分配明确、体制层级交错、智能情报决策和动态全景监管四个功能,提出构建国家矿产资源管理新模式的实现路径。

基于关键视图的文本驱动3D场景编辑方法

基于去噪扩散模型的零样本图像编辑方法取得了瞩目的成就,将之应用于3D场景编辑可实现零样本的文本驱动3D场景编辑。然而,其3D编辑效果容易受扩散模型的3D连续性与过度编辑等问题影响,产生错误的编辑结果。针对这些问题,提出了一种新的文本驱动3D编辑方法,该方法从数据端着手,提出了基于关键视图的数据迭代方法与基于像素点的异常数据掩码模块。关键视图数据可以引导一个3D区域的编辑以减少3D不一致数据的影响,而数据掩码模块则可以过滤掉2D输入数据中的异常点。使用该方法,可以实现生动的照片级文本驱动3D场景编辑效果。实验证明,相较于一些目前先进的文本驱动3D场景编辑方法,可以大大减少3D场景中错误的编辑,实现更加生动的、更具真实感的3D编辑效果。此外,使用该方法生成的编辑结果更具多样性、编辑效率也更高。

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。

轨道平顺性检测方法现状及发展综述

概述3种常见的轨道平顺性检测方法,从数据驱动与模型驱动2个方面对轨道平顺性车载检测方法的研究和发展进行详细评述,并分析其面临的主要挑战。数据驱动方法多应用于铁路系统健康状态检测,在轨道不平顺动态检测及轨下结构变形提取方面的研究正逐步展开。在模型驱动方法中,逆模型法目前主要用于获取轨道不平顺的频域特征;卡尔曼滤波类方法相比于惯性基准法具有融合多个传感器数据来提升轨道不平顺检测精度的优势。未来的研究应将物理模型和机制引入数据驱动的机器学习和深度学习模型中,在减少训练样本的情况下保证轨道平顺性的预测精度。多种传感器数据融合的动态检测,以及从动态不平顺中分离出不同成分,也是未来亟需发展的技术。

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.

AI大模型驱动下的教学短视频开发与实践

随着人工智能(Artificial Intelligence,AI)技术的飞速发展,AI大模型已成为教育创新的重要驱动力。深入剖析AI大模型的内涵与特点,探讨AI大模型驱动下开发教学短视频应遵循的基本原则,系统地阐述利用AI大模型精准生成教学短视频的策略和方法,以期提升教学短视频的制作品质与效率,加速推进教育资源智能化开发与精细化开发水平。

场景驱动下新零售商业模式创新及其价值创造的机理与策略

数字技术颠覆了零售行业的商业模式,通过重塑人、货、场关系驱动的新零售场景,为零售商与消费者带来全新的价值体验,也为我国商品经济发展带来新活力。在文献梳理的基础上,界定新零售场景和场景价值的内涵,识别出社群、内容、时空等场景要素,分析功能、效率、体验、情绪等场景价值的构成维度并探究其互动关系,构建“关系—场景—价值”概念模型。此外,引入商业模式创新的概念,并将其作为价值创造的抓手,以“场景—模式—价值”为主线,建构场景驱动下新零售商业模式创新的价值创造机理理论框架,提出“应用场景新颖化创造功能价值”“交易场景时空化创造效率价值”“互动场景内容化创造体验价值”“关注场景社群化创造情绪价值”等4条创造场景价值的策略。最后,以步步高为样本开展案例分析,为价值创造机理理论提供经验证据。研究丰富了商业模式场景化理论,也为新零售企业开展商业模式创新提供了理论参考。

新工科教育背景下高校工科生的数学成熟度探究

新工科教育背景下,数学成熟度是工程教育和高校工科生职业成功的关键因素。数学成熟度定义为使学生能够解决复杂问题、批判性思考和有效沟通的数学理解能力和熟练程度。探讨数学成熟度对工科学生的重要性,并提出工科学生数学成熟度的自我提高和培养策略。培养策略包括案例教学、融入数学建模、引入计算机辅助教学、采用问题驱动教学法、跨学科交流。详细讨论了每项策略,并提供了具体实例说明实施办法。旨在为高校工科生学习提供建议;为工程教育领域的教育工作者和研究人员提供参考,并促进开发有效的干预措施,以提高工科专业学生的数学成熟度。