Advanced Engineering Informatics最新文献_第10页

A method for constructing an ergonomics evaluation indicator system for community aging services based on Kano-Delphi-CFA: A case study in China 基于Kano-Delphi-CFA的社区养老服务工效评价指标体系构建方法：中国案例研究

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102842

Yixuan Liu , Jinchun Wu , Qianshu Fu , Haixia Feng , Jiao Liu , Yicheng Fang , Yafeng Niu , Chengqi Xue

To enhance the construction quality of community aging services, promote their standardization process, and improve the elderly’s experience, this study proposes a method that combines Kano model, Delphi method, and confirmatory factor analysis for constructing an ergonomics evaluation indicator system for aging services. By comparing the needs of elderly users and expert knowledge through the Kano model and Delphi method, key community aging service resource items are identified, leading to the construction of an ergonomics evaluation indicator system for community aging services. The relationships between indicators, factors, and common factors within the system are explored using confirmatory factor analysis to test the reliability of the indicator system. The results indicate that the community aging service ergonomics evaluation indicators can be summarized into nine factors: risk control in service process, reliability of service process, risk perception in service process, universal design of service, convenience of service process, effectiveness of service, inclusive design of service, load of service process, and comfort of service process. Additionally, these factors can be further distilled into three common factors: safety, efficiency, and well-being. The aging service ergonomics indicator system construction method proposed in this study contributes to the standardization of community aging services. It provides references and guidelines for the construction of aging services in communities.

为提升社区老龄化服务的建设质量，促进社区老龄化服务的标准化进程，改善老年人的服务体验，本研究提出了结合卡诺模型、德尔菲法和确证因素分析法构建老龄化服务工效评价指标体系的方法。通过卡诺模型和德尔菲法比较老年用户需求和专家知识，确定社区老龄服务资源的关键项目，从而构建社区老龄服务人机工程学评价指标体系。利用确证因子分析法探讨了指标、因子和系统内共性因子之间的关系，检验了指标体系的可靠性。结果表明，社区养老服务工效评价指标可归纳为九个因子：服务过程风险控制、服务过程可靠性、服务过程风险感知、服务通用设计、服务过程便利性、服务有效性、服务包容性设计、服务过程负荷、服务过程舒适性。此外，这些因素还可以进一步提炼为三个共同因素：安全、效率和幸福。本研究提出的老龄化服务工效指标体系构建方法有助于社区老龄化服务的标准化。为社区老龄服务建设提供参考和指引。

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引用次数: 0

Multiscale cost-sensitive learning-based assembly quality prediction approach under imbalanced data 不平衡数据下基于多尺度成本敏感学习的装配质量预测方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102860

Tianyue Wang , Bingtao Hu , Yixiong Feng , Hao Gong , Ruirui Zhong , Chen Yang , Jianrong Tan

Assembly quality prediction of complex products is vital in modern smart manufacturing systems. In recent years, data-driven approaches have obtained various outstanding engineering achievements in quality prediction. However, the imbalanced quality label makes it difficult for conventional quality prediction methods to learn accurate decision boundaries, resulting in weak prediction capabilities. Moreover, the multiple working condition data information in the assembly system presents another challenge to quality prediction. To handle the above issues, a multiscale cost-sensitive learning-based assembly quality prediction approach is proposed in this paper. First, an improved Gaussian mixture model is developed to automatically partition the global multi-condition data into several diverse subspaces. Then, the local cost-sensitive learning models are employed to tackle imbalanced data in each subspace. Finally, by leveraging Bayesian inference, multiple local cost-sensitive learning models are integrated to obtain a global multiscale prediction model. To validate the effectiveness of the proposed method, the quality prediction comparative experiments are conducted on two real-world assembly systems. The favorable results demonstrate the superiority of the proposed method in assembly quality prediction.

复杂产品的装配质量预测在现代智能制造系统中至关重要。近年来，数据驱动方法在质量预测领域取得了各种杰出的工程成就。然而，由于质量标签的不平衡性，传统的质量预测方法很难学习到准确的决策边界，导致预测能力较弱。此外，装配系统中的多种工况数据信息也给质量预测带来了挑战。针对上述问题，本文提出了一种基于多尺度成本敏感学习的装配质量预测方法。首先，开发了一种改进的高斯混合模型，可将全局多工况数据自动划分为多个不同的子空间。然后，采用局部成本敏感学习模型来处理每个子空间中的不平衡数据。最后，利用贝叶斯推理，整合多个局部成本敏感学习模型，得到全局多尺度预测模型。为了验证所提方法的有效性，我们在两个实际装配系统上进行了质量预测对比实验。良好的结果证明了所提方法在装配质量预测方面的优越性。

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引用次数: 0

Welding defect detection based on phased array images and two-stage segmentation strategy 基于相控阵图像和两阶段分割策略的焊接缺陷检测

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102879

Yan Chen , Deqiang He , Suiqiu He , Zhenzhen Jin , Jian Miao , Sheng Shan , Yanjun Chen

The rail transit vehicle body is composed of numerous welded structures, and to prevent failures during operation, it is essential that each weld undergoes strict and accurate quality inspection. Integrating segmentation algorithms with phased array ultrasonic testing (PAUT) offers a novel solution for the quality inspection of train welds. However, due to the high sensitivity of the phased array method in detecting weld defects, erroneous signals may be generated in non-welding areas, interfering with the judgment of deep learning algorithms and leading to incorrect detection results. To address the issue of existing algorithms being unable to completely eliminate false signals, this paper proposes a welding defect segmentation network with regional determination capabilities, which leverages both the defects and valid regions in phased array welding images. The concept of the proposed region determination performance is founded on establishing region-type rules for the defect detection task. Specifically, it involves the design of a two-stage network to assist in formulating the rules, along with a determination module to refine them. To assess the rationality and effectiveness of the proposed method, various parameters and modules of the model undergo extensive testing. The experimental results demonstrate that by splitting the defects and the valid regions in phased array welding images, reasonable and necessary determination rules can be constructed. This approach leads to more efficient and accurate weld defect segmentation.

轨道交通车体由许多焊接结构组成，为防止运行过程中出现故障，必须对每个焊缝进行严格准确的质量检测。将分割算法与相控阵超声波检测（PAUT）相结合，为列车焊缝质量检测提供了一种新的解决方案。然而，由于相控阵方法在检测焊缝缺陷时灵敏度较高，非焊接区域可能会产生错误信号，干扰深度学习算法的判断，导致检测结果不正确。针对现有算法无法完全消除错误信号的问题，本文提出了一种具有区域判定能力的焊接缺陷分割网络，它同时利用了相控阵焊接图像中的缺陷和有效区域。所提出的区域判定性能概念建立在为缺陷检测任务建立区域类型规则的基础上。具体来说，它包括设计一个两阶段网络来协助制定规则，以及一个确定模块来完善规则。为了评估所提出方法的合理性和有效性，对模型的各种参数和模块进行了广泛的测试。实验结果表明，通过分割相控阵焊接图像中的缺陷和有效区域，可以构建合理且必要的判定规则。这种方法可实现更高效、更准确的焊接缺陷分割。

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引用次数: 0

Human-in-the-loop optimization for vehicle body lightweight design 车身轻量化设计的人在环优化

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102887

Jia Hao , Ruofan Deng , Liangyue Jia , Zuoxuan Li , Reza Alizadeh , Leili Soltanisehat , Bingyi Liu , Zhibin Sun , Yiping Shao

Automatic optimization algorithms are crucial for vehicle body lightweight design; however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive optimization strategies partially mitigate this issue but lack a broad range of manipulation points and auxiliary information models. As such, we introduce a novel approach, “Human-in-the-Loop based method for Vehicle Body Lightweight Design” (HIL-VBLD). This method integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) an innovative interaction mode that provides multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI); (2) A comprehensive auxiliary information model that supports decision-making for designers. Our analysis demonstrates HIL-VBLD’s efficacy, showing a 54.5 % reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5 % mass reduction, mitigating local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model achieved a further 1.25 % mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9 % fewer iterations.

自动优化算法对于车身轻量化设计至关重要；然而，现有方法仍然效率低下，导致迭代次数过多，增加了时间和成本。目前的交互式优化策略可以部分缓解这一问题，但缺乏广泛的操作点和辅助信息模型。因此，我们引入了一种新方法，即 "基于人在回路的车身轻量化设计方法"（HIL-VBLD）。该方法将人工决策与优化算法相结合，以减少非生产性迭代。HIL-VBLD 包括两个关键部分：(1) 创新的交互模式，提供多个操作点，包括修改约束、切换算法和选择感兴趣的解决方案（SOI）；(2) 全面的辅助信息模型，支持设计人员的决策。我们的分析表明了 HIL-VBLD 的功效，使用 SOI 选择遗传算法的迭代周期减少了 54.5%。算法切换使质量减少了 4.5%，减少了与梯度算法相关的局部最优陷阱。此外，辅助信息模型进一步降低了 1.25% 的质量，增强了优化的稳健性。与传统的自动算法切换策略相比，HIL-VBLD 以减少 23.9% 的迭代次数保持了同等的精度。

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引用次数: 0

Phase-based video vibration measurement and fault feature extraction method for compound faults of rolling bearings 针对滚动轴承复合故障的相位视频振动测量和故障特征提取方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102897

Cong Li, Jun Zhou, Xing Wu, Tao Liu

Vibration characterization of rotating machinery is crucial for determining rotational and failure frequencies. Traditional contact measurement methods have limitations, while high-speed cameras offer a non-contact alternative for measuring target vibrations, spatial phase-based techniques have recently been widely used in detecting subtle vibrations and show good robustness to imaging noise. In this paper, a vision-based vibration extraction method for rotating machinery is proposed, aiming at extracting minor vibrations of bearings from them and further analyzing their fault frequencies. To address the challenge of separating a single fault frequency from the extracted compound faulty vibration signals, kurtosis is employed to analyze the inverse convolution period of multiple periodic components, and the MOMEDA filter length is optimized using the golden section algorithm. MOMEDA is then used to enhance each periodic pulse separately, and fault frequency is obtained through envelope spectrum demodulation. In the experimental part, a phase-based method is used to extract minor vibration displacements from rotor vibration video, which is subsequently compared with eddy current sensors in both time and frequency domains to verify the accuracy of the proposed method in extracting vibration displacements based on vision. Finally, vibration signals are extracted from the bearing compound fault video, and the single fault frequency characteristics of the bearing are successfully separated using the adaptive MOMEDA method, which provides an efficient and reliable method in the field of rotating machinery fault diagnosis.

旋转机械的振动表征对于确定旋转频率和故障频率至关重要。传统的接触式测量方法有其局限性，而高速相机为测量目标振动提供了一种非接触式的替代方法，基于空间相位的技术最近被广泛用于检测微小振动，并对成像噪声表现出良好的鲁棒性。本文提出了一种基于视觉的旋转机械振动提取方法，旨在从中提取轴承的微小振动，并进一步分析其故障频率。为解决从提取的复合故障振动信号中分离单一故障频率的难题，本文采用峰度分析多个周期成分的反卷积周期，并使用黄金分割算法优化 MOMEDA 滤波器长度。然后使用 MOMEDA 分别增强每个周期脉冲，并通过包络谱解调获得故障频率。在实验部分，使用基于相位的方法从转子振动视频中提取微小振动位移，随后与涡流传感器在时域和频域进行比较，以验证所提方法在基于视觉提取振动位移方面的准确性。最后，从轴承复合故障视频中提取振动信号，利用自适应 MOMEDA 方法成功分离出轴承的单一故障频率特性，为旋转机械故障诊断领域提供了一种高效可靠的方法。

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引用次数: 0

Automated construction site layout design system for prefabricated buildings using transformer based conditional GAN 使用基于变压器的条件 GAN 自动设计预制建筑施工现场布局系统

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102885

Yingnan Yang , Chunxiao Chen , Tao Li

Construction site layout plans (CSLP) are crucial for efficient prefabricated construction project management. Traditional manual design process is costly and time-consuming, while optimization methods heavily depend on expert knowledge. Recent advancements in deep generative models present promising alternatives. However, their application to the generation of prefabricated construction site layouts is hindered by several challenges, including limited datasets, significant overlap between facilities, and the necessity to generate layouts based on fixed facilities with specific attributes such as minimal transportation costs. These challenges constrain the efficacy and applicability of the generated layouts. To address these issues, this study introduces an innovative automated generative design system for prefabricated construction site layouts, leveraging a novel Transformer-based conditional generative adversarial network (GAN). The data preparation module of the system collects and augments layout data for training. The CSLGAN module is designed to generate layouts that conform to spatial constraints and desired attributes, with minimal facility overlap. Furthermore, this study establishes benchmarks in terms of model capacity and specialized performance metrics. Extensive experiments demonstrate the effectiveness of the proposed system in automated construction site layout generation.

施工现场平面布置图（CSLP）对于高效的预制建筑项目管理至关重要。传统的手工设计过程既费钱又费时，而优化方法则严重依赖专家知识。深度生成模型的最新进展为我们提供了前景广阔的替代方案。然而，它们在生成预制建筑工地布局方面的应用受到了一些挑战的阻碍，包括有限的数据集、设施之间的大量重叠，以及必须根据具有特定属性（如最低运输成本）的固定设施生成布局。这些挑战限制了生成布局的有效性和适用性。为了解决这些问题，本研究利用基于变换器的新型条件生成式对抗网络（GAN），为预制建筑工地布局引入了一个创新的自动生成设计系统。该系统的数据准备模块收集和扩充布局数据，用于训练。CSLGAN 模块旨在生成符合空间约束和所需属性的布局，并尽量减少设施重叠。此外，本研究还建立了模型容量和专门性能指标方面的基准。广泛的实验证明了拟议系统在自动生成建筑工地布局方面的有效性。

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引用次数: 0

Customized obstacle detection system for High-Speed Railways: A novel approach toward intelligent rail transportation 高速铁路定制障碍物检测系统：实现智能轨道交通的新方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102911

Leran Chen , Ping Ji , Yongsheng Ma , Yiming Rong , Jingzheng Ren

With the rapid advancement of rail transportation technology, particularly in high-speed rail, efficient and accurate obstacle detection is a crucial research focus. Traditional methods often depend on extensive datasets and complex computations, necessitating high-performance GPUs, which escalate hardware costs and power consumption. Moreover, these approaches may struggle with real-time performance and robustness.

To address these challenges, we propose a novel approach termed the “Customized Obstacle Detection System (CODS)” for high-speed railways. CODS swiftly and precisely identifies non-track elements by analyzing discrepancies between real-time sensor data and a predefined background model of an obstacle-free track. The proposed system is composed of three main components: constructing a prototypical rail environment, analyzing discrepancies to detect obstacles, and implementing a self-supervised mapping update with distributed storage.

Experimental results demonstrate that CODS significantly enhances obstacle detection, achieving a 10% increase in detection mean average precision and a 75% improvement in detection speed under various railway conditions. This research offers a robust, efficient solution for obstacle detection, contributing to the development of intelligent rail transportation systems.

随着轨道交通技术的飞速发展，尤其是在高速铁路领域，高效准确的障碍物检测成为研究的重点。传统方法通常依赖于大量数据集和复杂计算，需要使用高性能 GPU，从而增加了硬件成本和功耗。为了应对这些挑战，我们提出了一种适用于高速铁路的新方法，即 "定制障碍物检测系统（CODS）"。CODS 通过分析实时传感器数据与预定义的无障碍轨道背景模型之间的差异，迅速而精确地识别非轨道元素。实验结果表明，CODS 显著增强了障碍物检测能力，在各种铁路条件下，检测平均精度提高了 10%，检测速度提高了 75%。这项研究为障碍物检测提供了一种稳健、高效的解决方案，有助于智能轨道交通系统的发展。

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引用次数: 0

An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach 基于生成式人工智能和 Shrinkwrap 建模的 CAD 到 XR 自动框架，用于以用户为中心的设计方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-09-27 DOI: 10.1016/j.aei.2024.102848

Riccardo Rosati , Paolo Senesi , Barbara Lonzi , Adriano Mancini , Marco Mandolini

CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes.

CAD-to-XR 是指从计算机辅助设计 (CAD) 模型生成用于扩展现实 (XR) 应用程序的交互式逼真虚拟原型 (iPVP) 的工作流程。这一过程包括建模、贴图和 XR 编程。在文献中，还没有一种自动 CAD 到 XR 框架能同时管理 CAD 简化和纹理制作。在用户中心设计 (UCD) 中也没有采用这些框架的实例。此外，这种 CAD 到 XR 的工作流程也没有利用生成算法的潜力来生成合成图像（纹理）。本文介绍了一个实施 CAD 到 XR 工作流程的框架。该解决方案包括一个基于生成对抗网络（GAN）的纹理生成模块。生成的纹理随后由另一个模块（基于 Shrinkwrap 建模）管理，通过简化 3D 模型和对生成的纹理进行 UV 贴图来开发 iPVP。几何和材料数据被集成到一个图形引擎中，这样就可以在 XR 中与 iPVP 进行交互式编程。CAD 到 XR 框架在运动步枪的两个部件（枪托和前端）上进行了验证。与手动程序相比，该解决方案可以在更短的时间内自动完成不同产品版本的纹理制作过程。每次产品修订后，它都能避免生成新 iPVP 所需的繁琐手工操作。图像质量指标突出表明，图像是以 "逼真 "的方式生成的（生成纹理的感知质量与真实图像非常接近）。通过建议的框架生成的 iPVP，用户通过混合现实头戴式显示器进行可视化，其质量与传统设计的原型相当。

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引用次数: 0

A generative-AI-based design methodology for car frontal forms design 基于生成式人工智能的汽车正面造型设计方法学

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-09-26 DOI: 10.1016/j.aei.2024.102835

Peng Lu , Shih-Wen Hsiao , Jian Tang , Fan Wu

With the advancement of artificial intelligence, big data, and cloud computing, numerous generative AI applications have surfaced. In contrast to conventional generative design and computer-aided design tools, these applications significantly enhance design productivity. However, there are currently few design methodologies based on generative AIs in the academic community to improve the efficiency of industrial designers and optimize the design process. This study introduces a creative and practical methodology for designing car frontal forms based on generative AIs. In this methodology, imagery adjectives describing car frontal forms are generated by sending valid prompts to the text-generative AI application GPT-4.0. Then input typical imagery adjectives into the image-generative AI Midjourney successively as prompts to generate many car frontal forms that align with the typical imagery adjectives, forming a reference form database. Subsequently, a base form is selected, and target imageries are defined. Simultaneously, forms from the reference form database, conforming to the target imageries, are chosen as the reference forms. The main form elements of the base and reference forms are then delineated using cubic Bézier curves. Finally, a form curve blending algorithm is applied to obtain a set of alternatives, and the image-generative AI application Vega AI is utilized to convert the alternatives into three-dimensional renderings. FAHP-based expert evaluation and consumer perceptual evaluation are employed to validate the alternatives. Results indicate that the alternatives effectively capture the imageries of the reference forms. The proposed generative-AI-based design methodology enhances the efficiency of industrial designers, thereby minimizing human and material costs in product development. Additionally, this study presents a design case using various generative AIs to inspire designers to re-examine the traditional design process.

随着人工智能、大数据和云计算的发展，大量生成式人工智能应用浮出水面。与传统的生成式设计和计算机辅助设计工具相比，这些应用大大提高了设计效率。然而，目前学术界很少有基于生成式人工智能的设计方法来提高工业设计师的效率和优化设计流程。本研究介绍了一种基于生成式人工智能的汽车正面造型设计方法，具有创造性和实用性。在该方法中，通过向文本生成型人工智能应用程序 GPT-4.0 发送有效提示，生成描述汽车正面造型的意象形容词。然后向图像生成人工智能 Midjourney 连续输入典型的意象形容词作为提示，生成许多与典型意象形容词一致的汽车正面形态，形成参考形态数据库。随后，选择基本形式并定义目标图像。同时，从参考表格数据库中选择符合目标意象的表格作为参考表格。然后，使用立方贝塞尔曲线对基本形式和参考形式的主要形式元素进行划分。最后，应用形式曲线混合算法获得一组备选方案，并利用图像生成人工智能应用 Vega AI 将备选方案转换为三维效果图。采用基于 FAHP 的专家评估和消费者感知评估来验证备选方案。结果表明，备选方案有效地捕捉到了参考形式的图像。所提出的基于生成式人工智能的设计方法提高了工业设计师的工作效率，从而最大限度地降低了产品开发过程中的人力和物力成本。此外，本研究还介绍了一个使用各种生成式人工智能的设计案例，以启发设计师重新审视传统的设计流程。

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引用次数: 0

AdvMOB: Interactive visual analytic system of billboard advertising exposure analysis based on urban digital twin technique AdvMOB：基于城市数字孪生技术的广告牌广告曝光分析交互式视觉分析系统

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-09-25 DOI: 10.1016/j.aei.2024.102829

Qing Yu , Defan Feng , Ge Li , Qi Chen , Haoran Zhang

Digital outdoor billboards have emerged as powerful marketing tools for engaging consumers. However, the industry grapples with challenges, notably in accurately gauging exposure and understanding consumer demographics. Current ad placement models often overlook ad effectiveness and oversimplify advertising influence, resulting in a significant disparity between decision-making and real-world impact. To tackle these hurdles, this study introduces an innovative interactive visual analytic system, AdvMOB, designed to evaluate and scrutinize billboard exposure within urban landscapes and adeptly delineate the genuine reach of each advertisement. It integrates personal information and trajectory data to characterize the authentic impact of individual billboards. Through the fusion of urban architectural insights, and human mobility data, this system comprehensively evaluates, compares, and delves into the depth of billboard exposure in urban settings via an intuitive interface. The proposed AdvMOB system demonstrates substantial potential in revolutionizing the design of billboard advertisements by offering nuanced insights and comprehensive support.

数字户外广告牌已成为吸引消费者的强大营销工具。然而，该行业也面临着各种挑战，尤其是在准确衡量曝光率和了解消费者人口统计数据方面。当前的广告投放模式往往忽视广告效果，过度简化广告影响，导致决策与实际影响之间存在巨大差距。为了解决这些问题，本研究引入了一个创新的交互式视觉分析系统 AdvMOB，旨在评估和检查城市景观中的广告牌曝光率，并巧妙地划定每个广告的真正覆盖范围。该系统整合了个人信息和轨迹数据，以描述单个广告牌的真实影响。通过融合城市建筑洞察力和人类移动数据，该系统通过直观的界面对城市环境中广告牌的曝光深度进行全面评估、比较和深入研究。通过提供细致入微的洞察力和全面的支持，拟议的 AdvMOB 系统在彻底改变广告牌广告设计方面展示了巨大的潜力。

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引用次数: 0