Computers in Industry最新文献_第9页

From Ontologies to Knowledge Augmented Large Language Models for Automation: A decision-making guidance for achieving human–robot collaboration in Industry 5.0 从本体到面向自动化的知识增强大型语言模型：工业5.0中实现人机协作的决策指导

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-01 Epub Date: 2025-07-10 DOI: 10.1016/j.compind.2025.104329

John Oyekan , Christopher Turner , Michael Bax , Erich Graf

The rapid advancement of Large Language Models (LLMs) has resulted in interest in their potential applications within manufacturing systems, particularly in the context of Industry 5.0. However, determining when to implement LLMs versus other Natural Language Processing (NLP) techniques, ontologies or knowledge graphs, remains an open question. This paper offers decision-making guidance for selecting the most suitable technique in various industrial contexts, emphasizing human–robot collaboration and resilience in manufacturing. We examine the origins and unique strengths of LLMs, ontologies, and knowledge graphs, assessing their effectiveness across different industrial scenarios based on the number of domains or disciplines required to bring a product from design to manufacture. Through this comparative framework, we explore specific use cases where LLMs could enhance robotics for human–robot collaboration, while underscoring the continued relevance of ontologies and knowledge graphs in low-dependency or resource-constrained sectors. Additionally, we address the practical challenges of deploying these technologies, such as computational cost and interpretability, providing a roadmap for manufacturers to navigate the evolving landscape of Language based AI tools in Industry 5.0. Our findings offer a foundation for informed decision-making, helping industry professionals optimize the use of Language Based models for sustainable, resilient, and human-centric manufacturing. We also propose a Large Knowledge Language Model architecture that offers the potential for transparency and configuration based on complexity of task and computing resources available.

大型语言模型（llm）的快速发展引起了人们对其在制造系统中的潜在应用的兴趣，特别是在工业5.0的背景下。然而，确定何时实现llm与其他自然语言处理（NLP）技术、本体或知识图相比，仍然是一个悬而未决的问题。本文为在各种工业环境中选择最合适的技术提供了决策指导，强调了制造中的人机协作和弹性。我们研究了法学硕士、本体和知识图谱的起源和独特优势，根据产品从设计到制造所需的领域或学科数量，评估了它们在不同工业场景中的有效性。通过这个比较框架，我们探索了法学硕士可以增强人机协作的机器人技术的具体用例，同时强调了本体和知识图在低依赖性或资源受限领域的持续相关性。此外，我们还解决了部署这些技术的实际挑战，例如计算成本和可解释性，为制造商在工业5.0中导航基于语言的人工智能工具的不断发展的前景提供了路线图。我们的研究结果为明智的决策提供了基础，帮助行业专业人士优化基于语言的模型的使用，以实现可持续、有弹性和以人为中心的制造。我们还提出了一个大型知识语言模型体系结构，该体系结构提供了基于任务复杂性和可用计算资源的透明度和配置的潜力。

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引用次数: 0

Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins 面向认知数字孪生的可互操作工业知识表示和推理的检索增强生成

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-01 Epub Date: 2025-06-26 DOI: 10.1016/j.compind.2025.104330

Dachuan Shi , Jianzhang Li , Olga Meyer , Thomas Bauernhansl

The escalating volume and complexity of digital data within the manufacturing sector highlight an urgent need for an efficient knowledge representation and inference solution. Traditional approaches, which often rely on ontologies, knowledge graphs, or digital twins (DTs) for knowledge representation, and rule-based algorithms for inference, are becoming insufficient. The emergence of generative AI, particularly large language models (LLM) and retrieval-augmented generation (RAG), offers a more efficient and intelligent alternative. However, the performance of an RAG system is heavily dependent on the quality of retrieval results, which can be compromised by domain-specific knowledge and retrieval distractors. To address this challenge, we propose to enhance RAG systems tailored for the manufacturing industry in two aspects. First, we utilize the Asset Administration Shell (AAS), which represents the German industrial perspective on cognitive DTs, to create a representation of assets and knowledge in standardized information models. This establishes a robust foundation for the retrieval sources. Second, we propose a contrastive selection loss (CSL) to fine-tune an open-source LLM to refine the retrieval results. Fine-tuned LLMs possess higher efficiency and accuracy on task- and domain-specific datasets, while the CSL further enhances the model's ability to distinguish true positives from similar distractors. The enhanced RAG system is demonstrated in a robotic work cell integration use case and evaluated through a novel evaluation protocol. Additionally, the retrieval effectiveness of the RAG system, specifically the LLM fine-tuned with CSL, is extensively validated through statistical experiments. The results confirm its superior performance over state-of-the-art methods, including GPT-4 with in-context learning prompts and other fine-tuned models.

制造业中不断增长的数字数据量和复杂性凸显了对高效知识表示和推理解决方案的迫切需求。传统的方法通常依赖于本体、知识图或数字孪生（DTs）来表示知识，以及基于规则的推理算法，这些方法已经变得不够用了。生成式人工智能的出现，尤其是大型语言模型（LLM）和检索增强生成（RAG），提供了一种更高效、更智能的替代方案。然而，RAG系统的性能在很大程度上依赖于检索结果的质量，这可能会受到领域特定知识和检索干扰因素的影响。为了应对这一挑战，我们建议从两个方面加强为制造业量身定制的RAG系统。首先，我们利用资产管理外壳（AAS），它代表了德国工业对认知dt的看法，在标准化信息模型中创建资产和知识的表示。这为检索源建立了坚实的基础。其次，我们提出了一种对比选择损失（CSL）来微调开源LLM以优化检索结果。微调llm在任务和领域特定数据集上具有更高的效率和准确性，而CSL进一步增强了模型区分真实阳性和类似干扰物的能力。增强的RAG系统在机器人工作单元集成用例中进行了演示，并通过一种新的评估协议进行了评估。此外，RAG系统的检索有效性，特别是与CSL微调的LLM，通过统计实验得到了广泛的验证。结果证实了它优于最先进的方法，包括具有上下文学习提示和其他微调模型的GPT-4。

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引用次数: 0

Moving-feature-driven label propagation for training data generation from target domains 从目标域生成训练数据的移动特征驱动标签传播

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-01 Epub Date: 2025-07-09 DOI: 10.1016/j.compind.2025.104335

Taegeon Kim , Wei-Chih Chern , Seokhwan Kim , Vijayan K. Asari , Hongjo Kim

Deep learning models often suffer from performance degradation when applied to construction sites that differ from the source domain due to their sensitivity to data distribution shifts. Although methods such as transfer learning, domain adaptation, and synthetic data generation have been explored to improve generalization, collecting and annotating data from new target domains remains a labor-intensive bottleneck. This study presents a self-training-based framework to generate training data for construction object detection in unlabeled target domains. The method identifies moving objects using optical flow estimation, propagates class labels through iterative self-training, and synthesizes realistic training images via image inpainting and copy-paste augmentation. Experimental results from four visually distinct construction scenes demonstrate that the proposed method significantly improves detection performance without relying on manually labeled target data. These findings contribute to advancing automated and scalable domain adaptation techniques for vision-based construction monitoring.

由于深度学习模型对数据分布变化的敏感性，当应用于与源域不同的建筑工地时，其性能往往会下降。虽然已经探索了迁移学习、领域适应和合成数据生成等方法来提高泛化，但从新的目标领域收集和注释数据仍然是一个劳动密集型的瓶颈。本文提出了一种基于自训练的框架，用于在未标记的目标域中生成用于建筑目标检测的训练数据。该方法利用光流估计识别运动目标，通过迭代自训练传播类标签，并通过图像绘制和复制粘贴增强合成逼真的训练图像。实验结果表明，该方法在不依赖人工标记目标数据的情况下显著提高了检测性能。这些发现有助于推进自动化和可扩展的领域适应技术，用于基于视觉的施工监测。

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引用次数: 0

Energy consumption forecasting in non-stationary machining processes based on a physics-guided transformer 基于物理导向变压器的非平稳加工过程能耗预测

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-01 Epub Date: 2025-06-17 DOI: 10.1016/j.compind.2025.104321

Meihang Zhang , Ruiping Wang

Predicting energy consumption in non-stationary machining processes is challenging due to data complexity, dynamic variations, and real-time requirements. This paper proposes a novel physics-guided Transformer model incorporating supervisory-compensatory mechanisms. Firstly, several data preprocessing and feature extraction techniques, including Lagrange interpolation, wavelet transform, and a combined approach of principal component analysis and correlation analysis, are employed to enhance data quality and identify key physical variable. Secondly, modeling accuracy and efficiency are improved by integrating physics-guided variables into the traditional Transformer model, leading to advancements in the existing de-stationary attention module. Finally, distinct training and prediction models are established, incorporating a self-supervised, self-compensation mechanism in the training phase. This mechanism utilizes ground truth for training convergence, with optimized model parameters subsequently applied to the prediction model, significantly enhancing predictive efficacy. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches, achieving improvements in energy consumption prediction accuracy of over 76 % for carbon fiber processing, 30 % for plastic, 32.7 % for aluminum, and 54.5 % for 45steel. The integration of physical principles with sequence modeling enables precise energy consumption forecasting in non-stationary machining, advancing both predictive accuracy and industrial energy management.

由于数据复杂性、动态变化和实时要求，预测非平稳加工过程中的能耗具有挑战性。本文提出了一种新的物理导向变压器模型，该模型包含了监督-补偿机制。首先，采用拉格朗日插值、小波变换、主成分分析与相关分析相结合的方法对数据进行预处理和特征提取，提高数据质量，识别关键物理变量；其次，通过将物理导向变量集成到传统的Transformer模型中，提高了建模的精度和效率，从而改进了现有的去平稳注意力模块。最后，建立了不同的训练和预测模型，并在训练阶段引入了自我监督、自我补偿机制。该机制利用ground truth进行训练收敛，并将优化后的模型参数应用到预测模型中，显著提高了预测效果。实验结果表明，所提出的方法优于最先进的方法，碳纤维加工的能耗预测精度提高了76% %，塑料加工的能耗预测精度提高了30% %，铝加工的能耗预测精度提高了32.7% %，钢加工的能耗预测精度提高了54.5 %。将物理原理与序列建模相结合，可以在非平稳加工中实现精确的能耗预测，从而提高预测精度和工业能源管理。

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引用次数: 0

Evaluating calibration of deep fault diagnostic models under distribution shift 分布移位下深断层诊断模型的标定评价

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-10-01 Epub Date: 2025-07-04 DOI: 10.1016/j.compind.2025.104334

Yiming Xiao , Haidong Shao , Bin Liu

Current intelligent fault diagnosis studies focus on improving model accuracy. While accuracy is crucial, an exclusive emphasis on this metric can leave users oblivious to potentially untrustworthy decisions made by the model. This underscores the importance of confidence estimation and brings the model miscalibration problem to the forefront, i.e., the softmax probability, which is supposed to indicate the likelihood of the predicted label being correct but fails to reflect the true probability accurately. Addressing this issue is imperative for several reasons. Firstly, a well-calibrated model can provide users with an assessment of the risk associated with prediction failures, thereby withholding decision-making when the confidence is low and mitigating the risk of erroneous outputs. Especially in situations involving out-of-distribution (OOD) and distribution-shifted inputs, where the risk of model failure increases, the calibration property becomes even more critical. Secondly, well-calibrated confidence estimates can enhance users’ trust in today’s many black-box models. However, there have been limited fault diagnosis studies that specifically explore model calibration. The effectiveness of existing calibration methods in handling OOD and distribution-shifted inputs also remains unclear. Therefore, this paper evaluates multiple calibration methods and discusses their advantages and limitations, providing insights for subsequent studies. The results suggest that a deep ensemble method, which derives predictive expectations using multiple models with significantly different structures or parameters, has the potential to be the best calibration method. Code used in this paper is available at https://github.com/xiaoyiming1999/Calibration_for_RMFD.

目前的智能故障诊断研究主要集中在提高模型精度上。虽然准确性是至关重要的，但只强调这个指标可能会让用户忽略模型做出的可能不可信的决策。这强调了置信度估计的重要性，并将模型误校准问题带到了最前沿，即softmax概率，它应该表明预测标签正确的可能性，但不能准确反映真实的概率。出于几个原因，解决这个问题是必要的。首先，一个校准良好的模型可以为用户提供与预测失败相关的风险评估，从而在置信度较低时保留决策并降低错误输出的风险。特别是在涉及分布外（OOD）和分布移位输入的情况下，模型失效的风险增加，校准特性变得更加关键。其次，校准良好的置信度估计可以增强用户对当今许多黑盒模型的信任。然而，专门探讨模型校准的故障诊断研究有限。现有的校准方法在处理OOD和分布移位输入方面的有效性仍然不清楚。因此，本文对多种校准方法进行了评估，并讨论了它们的优点和局限性，为后续研究提供见解。结果表明，利用结构或参数显著不同的多个模型推导预测期望的深度集成方法有可能成为最佳的校准方法。本文中使用的代码可在https://github.com/xiaoyiming1999/Calibration_for_RMFD上获得。

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引用次数: 0

SRLFormer: Single Retinex-based and low-light image guidance Transformer for low-light image enhancement SRLFormer：基于单一视黄醇的低光图像引导变压器，用于低光图像增强

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-09-01 Epub Date: 2025-05-27 DOI: 10.1016/j.compind.2025.104314

Bin Wang, Bini Zhang, Jinfang Sheng

In image enhancement for low-illumination images, deep learning methods based on the Retinex theory typically decompose the image into illumination and reflectance, followed by iterative optimization or the use of prior custom enhancements. The reflectance map is then approximated as the enhanced image by dividing the radiance by the illumination map. However, this approach does not account for the noise hidden in low-illumination images or introduced during the enhancement of illumination. Additionally, it may cause computational overflow and amplify noise when the illumination in certain regions approaches ”0”. Moreover, these methods often require cumbersome multi-stage training and rely solely on convolutional neural networks, indicating limitations in capturing long-range dependencies. This paper proposes an efficient single-stage framework named SRF(Retinex-based single-retinex-based framework based on Retinex). SRF first estimates the inverse illumination image, then enhances the image by multiplying the inverse illumination with the low-illumination image, resulting in an image with improved brightness but still containing noise. Finally, we design a low-illumination guided Transformer network, LGF (Low-Illumination Guided Transformer), which utilizes the low-illumination image to guide denoising, thus more comprehensively considering the edge and detail information of the enhanced image. By integrating the LGT into SRF, we obtain the proposed algorithm SRLFormer. Experimental results show that SRLFormer significantly outperforms state-of-the-art methods in both qualitative and quantitative experiments, and its potential practical value is also demonstrated in downstream tasks and applications.

在低照度图像的图像增强中，基于Retinex理论的深度学习方法通常将图像分解为照度和反射率，然后进行迭代优化或使用先前的自定义增强。然后将反射率图近似为增强图像，方法是将亮度除以照度图。然而，这种方法没有考虑到低照度图像中隐藏的噪声或在增强照度过程中引入的噪声。此外，当某些区域的照明接近“0”时，可能会导致计算溢出和放大噪声。此外，这些方法通常需要繁琐的多阶段训练，并且仅依赖于卷积神经网络，这表明在捕获远程依赖关系方面存在局限性。本文提出了一种高效的单阶段框架SRF（Retinex-based single-retinex-based framework based on Retinex）。SRF首先对逆照度图像进行估计，然后将逆照度与低照度图像相乘对图像进行增强，得到亮度有所提高但仍含有噪声的图像。最后，我们设计了一种低照度引导变压器网络LGF (low-illumination guided Transformer)，它利用低照度图像来引导去噪，从而更全面地考虑了增强图像的边缘和细节信息。通过将LGT积分到SRF中，得到了提出的SRLFormer算法。实验结果表明，SRLFormer在定性和定量实验中都明显优于目前最先进的方法，并且在下游任务和应用中也证明了其潜在的实用价值。

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引用次数: 0

Multi-style adversarial variational self-distillation in randomized domains for single-domain generalized fault diagnosis 面向单域广义故障诊断的随机域多风格对抗变分自蒸馏

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-09-01 Epub Date: 2025-05-24 DOI: 10.1016/j.compind.2025.104319

Fan Yang, Xiaofeng Liu, Chunbing Zhang, Lin Bo

As rotating machinery often operates under complex and variable harsh conditions, domain generalization-based fault diagnosis has been adopted to tackle the challenge of distribution shifts and unseen data in target domains. However, most existing methods depend on fully labeled data from multiple source domains to learn domain-invariant representations. In practice, collecting comprehensive labeled data across diverse working conditions is often impractical, resulting in data insufficiency and distribution inconsistencies. To address the challenging scenario in which only a single fully labeled source domain is available, this article proposes a multi-style adversarial variational self-distillation (MSAVSD) framework based on domain randomization for single-domain generalized fault diagnosis. First, a domain-randomized generation module is developed to adaptively generate samples following randomized distributions by integrating adaptive noise and multi-scale style learning, thereby enriching the synthetic data with diverse and informative fault representations. Next, a scale-enhanced feature extraction module is introduced to extract rich domain-invariant features, thereby maximizing the utilization of fault-related information under limited training conditions. The method suppresses task-irrelevant noise and redundancy via variational self-distillation and employs contrastive learning to enhance the discriminability and consistency of task-relevant features. Extensive diagnostic experiments on three datasets, two self-collected and one publicly available, demonstrate that the proposed method outperforms state-of-the-art methods.

由于旋转机械经常在复杂多变的恶劣条件下运行，基于域泛化的故障诊断被用于解决目标域中分布变化和未知数据的挑战。然而，大多数现有方法依赖于来自多个源域的完全标记数据来学习域不变表示。在实践中，在不同的工作条件下收集全面的标记数据通常是不切实际的，从而导致数据不足和分布不一致。为了解决只有一个完全标记的源域可用的具有挑战性的场景，本文提出了一种基于域随机化的多风格对抗变分自蒸馏（MSAVSD）框架，用于单域广义故障诊断。首先，通过集成自适应噪声和多尺度风格学习，开发了域随机生成模块，根据随机分布自适应生成样本，从而使合成数据丰富多样、信息丰富；其次，引入尺度增强特征提取模块，提取丰富的域不变特征，从而在有限的训练条件下最大限度地利用故障相关信息。该方法通过变分自蒸馏来抑制与任务无关的噪声和冗余，并利用对比学习来增强任务相关特征的可辨别性和一致性。在三个数据集上进行了广泛的诊断实验，两个数据集是自己收集的，一个数据集是公开的，表明所提出的方法优于最先进的方法。

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引用次数: 0

A novel paradigm for predicting and interpreting uneven roll wear in the hot rolling steel industry 预测和解释热轧钢行业轧辊不均匀磨损的新范式

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-09-01 Epub Date: 2025-05-21 DOI: 10.1016/j.compind.2025.104318

Wen Peng , Cheng-yan Ding , Yu Liu , Jia-nan Sun , Zhen Wei , Wen-bo Wang , Dian-hua Zhang , Jie Sun

In the hot rolling industry, uneven roll wear significantly influences schedule free rolling and product quality, necessitating more precise wear prediction to improve the capabilities of hot rolling production. However, existing methods, laden with limitations, struggle to predict uneven roll wear precisely and transparently. To address these challenges, we present a novel paradigm that combines a computer simulation technique, classical wear theory and a data-driven approach for predicting uneven work roll wear in the hot rolling industry. Initially, a finite element model is constructed to simulate hot rolling processing. Subsequently, an Archard-theory-based work roll wear model is derived to calculate the theoretical wear loss using the simulation results. Following this, based on the theoretical wear loss, a deep ensemble model containing three base predictors is established. Notably, Shapley additive explanations (SHAP) and ensemble mechanism analysis are implemented to explain the predictive process of the wear loss. The comparative experimental results demonstrate the deep ensemble method achieves a 2 % accuracy improvement over other machine learning models. Additionally, the wear prediction results for a real case of a roll change period prove that, at the peak position of wear profile, the proposed paradigm surpasses the existing model by 7.2 %. Significantly, the feature contributions and process interpretable analysis based on SHAP make the proposed paradigm both transparent and reliable.

在热轧行业中，轧辊磨损不均匀严重影响无进度轧制和产品质量，需要更精确的磨损预测来提高热轧生产能力。然而，现有的方法，充满了局限性，难以准确和透明地预测轧辊不均匀磨损。为了应对这些挑战，我们提出了一种新的范例，将计算机模拟技术、经典磨损理论和数据驱动方法相结合，用于预测热轧工业中工作辊的不均匀磨损。首先，建立了模拟热轧过程的有限元模型。在此基础上，建立了基于archard理论的工作辊磨损模型，利用仿真结果计算理论磨损损失。在此基础上，以理论磨损量为基础，建立了包含三个基本预测量的深度系综模型。值得注意的是，采用Shapley加性解释（SHAP）和系综机理分析来解释磨损的预测过程。对比实验结果表明，与其他机器学习模型相比，深度集成方法的准确率提高了2 %。此外，对实际轧辊换期的磨损预测结果表明，在磨损曲线的峰值位置，所提出的模型比现有模型高出7. %。值得注意的是，基于SHAP的特征贡献和过程可解释性分析使所提出的范式既透明又可靠。

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引用次数: 0

Survey of automated methods for design and assessment of smart products 智能产品设计和评估的自动化方法综述

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-09-01 Epub Date: 2025-05-24 DOI: 10.1016/j.compind.2025.104316

Anoop Kumar Sinha , Youngmi Christina Choi , David W. Rosen

User centric smart products prioritize the needs and preferences of users, enhancing their experience and satisfaction. Involving users in the design and assessment of smart products ensures that they meet real-world requirements, leading to more intuitive product design, user interface, and functionalities that truly resonate with users. Further, the capability of generating and evaluating many alternative designs early in product development is beneficial. However, the need to construct physical prototypes for user testing limits the number of designs that can be evaluated during early design stages. As such, our interest is in automated methods that support user centered design and usability and user experience assessment. In this review article, we look at at two decades of automation methods that have been employed in the design and development of user centric smart products. The focus of these automation methods is to incorporate user voice in early design stages rather than replacing the users. We have identified five key activities of the design cycle in which automated methods have been employed: design thinking, design ideation, prototype creation, user data collection for usability study, and user data analysis. Overall, 154 articles were identified across engineering, human-computer interaction, human factors, inclusive design, industrial design, and other disciplines that have incorporated automation methods to include the user’s voice in the design of user centric smart products. This review examines the effectiveness and limitations of different automation methods compared to conventional methods, offering valuable insights and suggestions to enhance the design processes of smart products with a focus on widespread usability issues. Our specific interest lies in developing assistive mobility and rehabilitation devices, where constraints such as limited development time and resources persist, yet the usability and user experience profoundly influence significant outcomes like perceived functionality, stigma, and device acceptance.

以用户为中心的智能产品优先考虑用户的需求和偏好，提高用户的体验和满意度。让用户参与智能产品的设计和评估，确保产品符合现实需求，从而使产品设计、用户界面和功能更加直观，真正与用户产生共鸣。此外，在产品开发早期生成和评估许多备选设计的能力是有益的。然而，为用户测试构建物理原型的需要限制了在早期设计阶段可以评估的设计的数量。因此，我们的兴趣在于支持以用户为中心的设计、可用性和用户体验评估的自动化方法。在这篇综述文章中，我们研究了二十年来在以用户为中心的智能产品的设计和开发中采用的自动化方法。这些自动化方法的重点是在早期设计阶段纳入用户的声音，而不是取代用户。我们已经确定了采用自动化方法的设计周期的五个关键活动：设计思维、设计构思、原型创建、可用性研究的用户数据收集和用户数据分析。总体而言，在工程、人机交互、人为因素、包容性设计、工业设计和其他学科中确定了154篇文章，这些学科已将自动化方法纳入以用户为中心的智能产品设计中，包括用户的声音。本文考察了与传统方法相比，不同自动化方法的有效性和局限性，为提高智能产品的设计过程提供了有价值的见解和建议，重点关注广泛的可用性问题。我们的具体兴趣在于开发辅助移动和康复设备，其中限制，如有限的开发时间和资源持续存在，但可用性和用户体验深刻地影响重大结果，如感知功能，污名和设备接受度。

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引用次数: 0

Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries 工业物联网：跨不同行业的实现、挑战和潜在解决方案

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-09-01 Epub Date: 2025-05-28 DOI: 10.1016/j.compind.2025.104317

Shaila Afrin , Sabiha Jannat Rafa , Maliha Kabir , Tasfia Farah , Md. Sakib Bin Alam , Aiman Lameesa , Shams Forruque Ahmed , Amir H. Gandomi

The Industrial Internet of Things (IIoT) has emerged as a potent catalyst for transformation across many industries as a part of Industry 4.0. This review thoroughly examines IIoT applications, demonstrating how it enhances operational efficiency, informed decision-making, cost optimization, innovation, and workplace safety. While prior research has often concentrated on technical dimensions such as fog and edge computing, network protocols, or big data integration, several emerging and high-impact application areas remain underexplored. This study addresses that gap by systematically reviewing IIoT implementations in critical yet often overlooked domains, including environmental monitoring, agriculture, construction, healthcare, robotics, smart grids, and predictive maintenance. It offers fresh insights into how IIoT is being adapted to meet real-world challenges in these sectors. In addition to outlining the current landscape, the review identifies core barriers such as data security, interoperability, and system scalability. It underscores the importance of cross-sector collaboration and strategic alignment to fully leverage the transformative potential of IIoT. The paper concludes by outlining key research gaps and future opportunities to guide continued innovation and scholarly investigation.

作为工业4.0的一部分，工业物联网（IIoT）已经成为许多行业转型的有力催化剂。本文全面考察了工业物联网的应用，展示了它如何提高运营效率、明智决策、成本优化、创新和工作场所安全。虽然之前的研究通常集中在雾和边缘计算、网络协议或大数据集成等技术层面，但一些新兴和高影响力的应用领域仍未得到充分探索。本研究通过系统地回顾工业物联网在关键但经常被忽视的领域的实施情况，包括环境监测、农业、建筑、医疗保健、机器人、智能电网和预测性维护，解决了这一差距。它为如何适应工业物联网以应对这些领域的现实挑战提供了新的见解。除了概述当前形势外，审查还确定了核心障碍，如数据安全性、互操作性和系统可伸缩性。它强调了跨部门合作和战略协调的重要性，以充分利用工业物联网的变革潜力。论文最后概述了关键的研究差距和未来的机会，以指导持续的创新和学术研究。

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引用次数: 0