Intelligent Systems with Applications最新文献_第6页

New Harris Hawks algorithms for the Close-Enough Traveling Salesman Problem 足够近旅行商问题的新Harris Hawks算法

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.iswa.2025.200586

Tansel Dokeroglu, Deniz Canturk

This study introduces a novel application of the Harris Hawks Optimization (HHO) algorithm to the Close-Enough Traveling Salesman Problem (CETSP), a challenging combinatorial optimization problem where circular neighborhoods rather than exact coordinates represent target points. To tackle the CETSP’s spatial complexity and high-dimensional solution space, we develop new HHO algorithms, including a parallel multi-population variant designed using the OpenMP framework. This parallel algorithm allows multiple subpopulations to evolve simultaneously, increasing diversity and computational efficiency, particularly on large-scale and real-time instances. Furthermore, new problem-specific exploration and exploitation operators are introduced, tailored to the CETSP’s geometric structure, enabling better guidance of the search process toward high-quality solutions. A comprehensive empirical evaluation is conducted on 47 benchmark instances, encompassing synthetic problem instances and a real-world robotic welding scenario in automotive manufacturing. The results show that the proposed methods outperform existing state-of-the-art techniques such as Genetic Algorithm (GA), Memetic Algorithm (MA-CETSP) and Variable Neighborhood Search (VNS)-based approaches, achieving 18 new best-known solutions. The experimental findings underline the strong convergence behavior, robustness across diverse problem sizes, and practical applicability of the algorithm. Additionally, the algorithm’s modular and extensible structure leads the way for future adaptations to multi-objective and dynamic versions of CETSP, broadening its relevance for both academic research and industrial deployment.

本文介绍了Harris Hawks Optimization （HHO）算法在近距离旅行商问题（CETSP）中的一种新应用，这是一个具有挑战性的组合优化问题，其中圆形邻域而不是精确坐标表示目标点。为了解决CETSP的空间复杂性和高维解空间，我们开发了新的HHO算法，包括使用OpenMP框架设计的并行多种群变体。这种并行算法允许多个子种群同时进化，增加了多样性和计算效率，特别是在大规模和实时实例上。此外，针对CETSP的几何结构，还引入了新的针对特定问题的勘探和开发操作方法，从而更好地指导搜索过程，以获得高质量的解决方案。对47个基准实例进行了全面的实证评估，其中包括汽车制造中的综合问题实例和实际机器人焊接场景。结果表明，所提出的方法优于现有的最先进的技术，如遗传算法（GA），模因算法（MA-CETSP）和基于变量邻域搜索（VNS）的方法，实现了18个新的最知名的解决方案。实验结果强调了该算法的强收敛性、跨不同问题规模的鲁棒性和实际适用性。此外，该算法的模块化和可扩展结构为未来适应多目标和动态版本的CETSP铺平了道路，扩大了其在学术研究和工业部署方面的相关性。

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引用次数: 0

Explainable Artificial Intelligence: A systematic Review of Progress and Challenges 可解释的人工智能：进步与挑战的系统回顾

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-10-27 DOI: 10.1016/j.iswa.2025.200595

Azza Mohamed , Khaled Abdelqader , Khaled Shaalan

This work employs a multidisciplinary approach to identify research gaps in the existing literature by presenting a systematic review of systematic reviews on Explainable Artificial Intelligence (XAI). To the best of our knowledge, this is the first thorough meta-review that combines the findings of several excellent reviews to offer a more elevated viewpoint on the goals and difficulties facing the area. The review covers empirical studies published between 2021 and 2023, focusing on high-quality sources. An initial pool of 997 entries was screened across multiple databases, yielding 928 unique articles after duplicate removal. Ultimately, 14 studies met the inclusion criteria and were analyzed in depth. The quality assessment confirmed that all selected reviews adhered to established methodological standards. The key findings show XAI's broad uses, which range from increasing trust and transparency to assisting with financial and management decision-making. The prevalence of healthcare-focused studies emphasizes XAI's importance in enhancing interpretability, fairness, regulatory compliance, and personalized treatment options. Commonly used techniques include visual explanation tools, interpretable machine learning models, and model-agnostic approaches. While the review offers valuable insights, it acknowledges limitations such as its reliance on Q1 journals and the exclusion of broader sources, which may affect comprehensiveness. To advance the field, the study recommends expanding future research to underrepresented domains like autonomous vehicles, defense, and smart cities. It also calls for methodological innovation to enhance accessibility, fairness, privacy, and the development of intuitive explanation strategies. Addressing these gaps can significantly improve the trustworthiness and effectiveness of AI systems across sectors.

这项工作采用多学科方法，通过对可解释人工智能（XAI）的系统综述进行系统综述，来确定现有文献中的研究空白。据我们所知，这是第一个全面的元综述，它结合了几篇优秀综述的发现，为该领域面临的目标和困难提供了一个更高的观点。该综述涵盖了2021年至2023年期间发表的实证研究，重点关注高质量来源。在多个数据库中筛选997个条目的初始池，删除重复后产生928个唯一条目。最终有14项研究符合纳入标准，并进行了深入分析。质量评估确认所有选定的审查都遵守既定的方法标准。主要发现表明，XAI的用途广泛，从增加信任和透明度到协助财务和管理决策。以医疗保健为重点的研究的流行强调了XAI在增强可解释性、公平性、法规遵从性和个性化治疗选择方面的重要性。常用的技术包括可视化解释工具、可解释的机器学习模型和模型不可知论方法。虽然这篇综述提供了有价值的见解，但它也承认其局限性，比如它依赖于Q1期刊和排除了更广泛的来源，这可能会影响全面性。为了推进该领域的发展，该研究建议将未来的研究扩展到无人驾驶汽车、国防和智能城市等代表性不足的领域。它还呼吁方法创新，以提高可访问性，公平性，隐私性，并发展直观的解释策略。解决这些差距可以显著提高各部门人工智能系统的可信度和有效性。

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引用次数: 0

A data-driven optimization approach for automated reviewer assignment using natural language processing 一种使用自然语言处理的数据驱动优化方法，用于自动审稿人分配

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-18 DOI: 10.1016/j.iswa.2025.200587

Meltem Aksoy , Seda Yanik , Mehmet Fatih Amasyali

In many settings, such as project or publication selection, expert reviewers play a pivotal role, as their assessments serve as the primary basis for determining a project's prospective value. The effectiveness of matching and assigning qualified experts to evaluate project proposals can substantially influence the quality of the selection process and, consequently, impact the funding organization's return on investment. Despite its importance, many funding organizations continue to rely on basic manual methods for assigning reviewers. This simplistic approach can compromise the quality of project selection and lead to suboptimal financial outcomes. Moreover, it may hinder the equitable distribution of review workloads and increase conflicts of interest between reviewers and applicants. Consequently, there is a pressing need for a systematic and automated method to enhance the reviewer assignment process.

In this study, we propose an optimization-based approach using natural language processing to automate the reviewer assignment process for project proposals. The proposed approach follows a structured three-stage methodology. First, a comprehensive database is constructed by collecting multilingual data on both proposals and reviewers. Second, word embedding techniques are used to represent texts as vectors, enabling the use of cosine similarity to quantify the relevance between each proposal and reviewer. Reviewer expertise and past evaluation performance are also analyzed using predefined knowledge rules. In the final stage, a multi-objective integer linear programming model assigns reviewers by optimizing proposal-reviewer similarity and reviewer competency while preventing conflicts of interest. Additionally, a max-min strategy is employed to ensure fair treatment of less-advantaged proposals, and two supplementary models are introduced to balance reviewer workloads. Experimental results on a real-world dataset from a regional development agency demonstrate that the proposed system significantly outperforms traditional manual assignment methods. We show that automated reviewer assignment prevents subjective judgements, together with reductions in time and cost of the assignment process.

在许多情况下，例如项目或出版物选择，专家审稿人扮演着关键的角色，因为他们的评估是确定项目预期价值的主要基础。匹配和分配合格专家来评估项目提案的有效性可以极大地影响选择过程的质量，从而影响资助组织的投资回报。尽管它很重要，但许多资助组织仍然依赖于基本的手工方法来分配审稿人。这种简单的方法可能会损害项目选择的质量，并导致次优的财务结果。此外，它可能阻碍审查工作量的公平分配，并增加审查者和申请人之间的利益冲突。因此，迫切需要一种系统和自动化的方法来增强审稿人分配过程。在这项研究中，我们提出了一种基于优化的方法，使用自然语言处理来自动化项目提案的审稿人分配过程。拟议的方法遵循结构化的三阶段方法。首先，通过收集提案和审稿人的多语种数据，构建一个全面的数据库。其次，使用词嵌入技术将文本表示为向量，从而可以使用余弦相似度来量化每个提案和审稿人之间的相关性。使用预定义的知识规则分析审稿人的专业知识和过去的评估绩效。最后，在避免利益冲突的同时，通过优化提案-审稿人相似性和审稿人能力，建立多目标整数线性规划模型分配审稿人。此外，采用了最大最小策略来确保公平对待劣势提案，并引入了两个补充模型来平衡审稿人的工作量。在一个区域发展机构的真实数据集上的实验结果表明，该系统显著优于传统的人工分配方法。我们展示了自动审稿人分配防止了主观判断，同时减少了分配过程的时间和成本。

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引用次数: 0

Optimizing the distribution of tasks in Internet of Things using edge processing-based reinforcement learning 利用基于边缘处理的强化学习优化物联网中的任务分配

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-14 DOI: 10.1016/j.iswa.2025.200585

Mohsen Latifi, Nahideh Derakhshanfard, Hossein Heydari

As the Internet of Things expands, managing intelligent tasks in dynamic and heterogeneous environments has emerged as a primary challenge for processing-based systems at the network’s edge. A critical issue in this domain is the optimal allocation of tasks. A review of prior studies indicates that many existing approaches either focus on a single objective or suffer from instability and overestimation of decision values during the learning phase. This paper aims to bridge this by proposing an approach that utilizes reinforcement learning with a double Q-learning algorithm and a multi-objective reward function. Furthermore, the designed reward function facilitates intelligent decision-making under more realistic conditions by incorporating three essential factors: task execution delay, energy consumption of edge nodes, and computational load balancing across the nodes. The inputs for the proposed method encompass information such as task sizes, deadlines for each task, remaining energy in the nodes, computational power of the nodes, proximity to the edge nodes, and the current workload of each node. The method's output at any given moment is the decision regarding assigning any task to the most suitable node. Simulation results in a dynamic environment demonstrate that the proposed method outperforms traditional reinforcement learning algorithms. Specifically, the average task execution delay has been reduced by up to 23%, the energy consumption of the nodes has decreased by up to 18%, and load balancing among nodes has improved by up to 27%.

随着物联网的扩展，管理动态和异构环境中的智能任务已经成为网络边缘处理系统面临的主要挑战。该领域的一个关键问题是任务的最佳分配。回顾以往的研究表明，许多现有的方法要么专注于单一目标，要么在学习阶段存在不稳定性和高估决策值的问题。本文旨在通过提出一种利用双q学习算法和多目标奖励函数的强化学习方法来解决这一问题。此外，设计的奖励函数结合了任务执行延迟、边缘节点能耗和节点间计算负载均衡三个基本因素，促进了更现实条件下的智能决策。该方法的输入包括任务大小、每个任务的截止日期、节点的剩余能量、节点的计算能力、与边缘节点的接近程度以及每个节点的当前工作负载等信息。该方法在任何给定时刻的输出是关于将任何任务分配给最合适节点的决策。动态环境下的仿真结果表明，该方法优于传统的强化学习算法。具体来说，平均任务执行延迟降低了23%，节点能耗降低了18%，节点间负载均衡提高了27%。

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引用次数: 0

Enhanced set-based particle swarm optimization for portfolio management in a walk-forward paradigm 改进的基于集合的粒子群优化组合管理方法

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.iswa.2025.200582

Zander Wessels , Andries Engelbrecht

A novel approach to portfolio optimization is introduced using a variant of set-based particle swarm optimization (SBPSO), building upon the foundational work of Erwin and Engelbrecht. Although their contributions advanced the application of SBPSO to financial markets, this research addresses key practical challenges, specifically enhancing the treatment of covariance and expected returns and refining constraint implementations to align with real-world applications. Beyond algorithmic improvements, this article emphasizes the importance of robust evaluation methodologies and highlights the limitations of traditional backtesting frameworks, which often yield overly optimistic results. To overcome these biases, the study introduces a comprehensive simulation platform that mitigates issues such as survivorship and forward-looking bias. This provides a realistic assessment of the modified SBPSO’s financial performance under varying market conditions. The findings shift the focus from computational efficiency to the practical outcomes of profitability that are most relevant to investors.

在Erwin和Engelbrecht的基础上，提出了一种新的基于集合的粒子群优化（SBPSO）的组合优化方法。尽管他们的贡献推动了SBPSO在金融市场的应用，但本研究解决了关键的实际挑战，特别是加强了协方差和预期收益的处理，并改进了约束实现，使其与现实世界的应用保持一致。除了算法改进之外，本文还强调了健壮的评估方法的重要性，并强调了传统回测框架的局限性，这些框架通常会产生过于乐观的结果。为了克服这些偏见，该研究引入了一个全面的模拟平台，以减轻诸如生存和前瞻性偏见等问题。这提供了一个现实的评估修改后的SBPSO的财务业绩在不同的市场条件下。研究结果将重点从计算效率转移到与投资者最相关的盈利能力的实际结果。

引用次数: 0

Attention-based fuzzy neural networks for self-supervised data annotation 基于注意力的模糊神经网络自监督数据标注

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-11-13 DOI: 10.1016/j.iswa.2025.200610

Md Rakibul Islam, Shahina Begum, Mobyen Uddin Ahmed, Shaibal Barua

Annotating vibration data from heavy-duty pumps in the mining industry is highly challenging because it demands domain knowledge, a complex inspection setup, and, in many cases, remains infeasible. A self-supervised data annotation (SSDA) framework is therefore proposed and evaluated on historical data of slurry-pump vibration signals. The framework began with the collection of heterogeneous information, followed by information fusion using an autoencoder. This was then followed by a datafication step for preprocessing and achieving a better representation of features through a feature embedding technique. As a result, redundant information was pushed into an eight-dimensional latent space, achieving a reconstruction loss of 0.0023. Furthermore, Initial data annotation was obtained by combining the Isolation Forest and Kneedle algorithms to locate a data-driven knee or threshold, and it was found to be 0.58 for predicting labels. Partial samples were labeled and considered accurate. Lastly, an attention-based fuzzy neural network (AFNN) is trained on those labels where membership functions convert each latent feature into graded truth values. At the same time, an attention layer highlights the most relevant rules. An iterative self-training loop was implemented to refine the training set and obtain labeled data with higher model confidence. Here, we also tested six baseline models and found AFNN quite impressive. After seven iterations 2780 of 2872 samples were labeled and the remaining 92 are considered uncertain, still need some review from an expert, and the AFNN model confidence was (96.8%). Statistical analysis confirmed that the model predictions were significantly associated with true labels (

p < 0.05

) and not driven by chance.

对采矿行业重型泵的振动数据进行注释是一项极具挑战性的工作，因为它需要领域知识和复杂的检测设置，而且在许多情况下仍然是不可行的。为此，提出了一种自监督数据注释（SSDA）框架，并对浆料泵振动信号历史数据进行了评价。该框架从异构信息的收集开始，然后使用自编码器进行信息融合。接下来是数据预处理步骤，并通过特征嵌入技术实现更好的特征表示。结果，冗余信息被推入八维潜在空间，重构损失为0.0023。此外，结合隔离森林和膝关节算法获得初始数据注释，以定位数据驱动的膝关节或阈值，发现预测标签的概率为0.58。部分样品被标记并被认为是准确的。最后，在这些标签上训练基于注意力的模糊神经网络（AFNN），其中隶属函数将每个潜在特征转换为分级真值。与此同时，注意力层突出了最相关的规则。采用迭代自训练循环对训练集进行细化，得到具有较高模型置信度的标记数据。在这里，我们还测试了六个基线模型，发现AFNN非常令人印象深刻。经过7次迭代，2872个样本中的2780个被标记，剩下的92个被认为是不确定的，仍然需要专家的一些审查，AFNN模型置信度为（96.8%）。统计分析证实，模型预测与真实标签显著相关（p<0.05），并非偶然驱动。

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引用次数: 0

Personalized privacy in OSNs: Evaluating deep learning models for context-aware image editing osn中的个性化隐私：评估上下文感知图像编辑的深度学习模型

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI: 10.1016/j.iswa.2025.200581

Gelareh Hasel Mehri , Georgi Kostov , Bernardo Breve , Andrei Jalba , Nicola Zannone

Online Social Networks (OSNs) have become a cornerstone of digital interaction, enabling users to easily create and share content. While these platforms offer numerous benefits, they also expose users to privacy risks such as cyberstalking and identity theft. To address these concerns, OSNs typically provide access control mechanisms that allow users to regulate content visibility. However, these mechanisms often assume that content is managed by individual users and focus primarily on preserving content integrity, which may discourage users from sharing sensitive information. In this work, we propose a privacy model that empowers users to conceal sensitive content in images according to their preferences, expressed by means of policies. Our approach employs a multi-stage pipeline that includes segmentation for object localization, scene graphs and distance metrics for determining object ownership, and inpainting techniques for editing. We investigate the use of advanced deep learning models to implement the privacy model, aiming to provide personalized privacy controls while maintaining high image fidelity. To evaluate the proposed model, we conducted a user study with 20 participants. The user study highlights that ownership is the most significant factor influencing user perceptions of policy enforcement compliance, with less impact from localization and editing. The results also reveal that participants are generally willing to adopt the fully automated privacy model for selectively editing images in OSNs based on viewer identity, although some prefer alternative use cases, such as editing or censorship tools. Participants also raised concerns about the potential misuse of the model, supporting our choice of excluding an option for object replacement.

在线社交网络（OSNs）已经成为数字交互的基石，使用户能够轻松地创建和共享内容。虽然这些平台提供了许多好处，但它们也使用户面临网络跟踪和身份盗用等隐私风险。为了解决这些问题，osn通常提供允许用户调节内容可见性的访问控制机制。然而，这些机制通常假设内容是由单个用户管理的，并且主要关注于保持内容的完整性，这可能会阻碍用户共享敏感信息。在这项工作中，我们提出了一个隐私模型，使用户能够根据自己的偏好隐藏图像中的敏感内容，并通过策略来表达。我们的方法采用多阶段管道，包括用于对象定位的分割，用于确定对象所有权的场景图和距离度量，以及用于编辑的绘图技术。我们研究了使用先进的深度学习模型来实现隐私模型，旨在提供个性化的隐私控制，同时保持高图像保真度。为了评估所提出的模型，我们对20名参与者进行了用户研究。用户研究强调，所有权是影响用户对政策执行合规性看法的最重要因素，本地化和编辑的影响较小。研究结果还显示，参与者普遍愿意采用全自动隐私模型，根据观看者身份选择性地编辑osn中的图像，尽管有些人更喜欢其他用例，如编辑或审查工具。与会者还提出了对模型可能被滥用的担忧，支持我们排除对象替换选项的选择。

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引用次数: 0

Advancing emergency vehicle systems with deep learning: A comprehensive review of computer vision techniques 用深度学习推进应急车辆系统：计算机视觉技术的综合综述

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-08-26 DOI: 10.1016/j.iswa.2025.200574

Ali Omari Alaoui, Othmane Farhaoui, Mohamed Rida Fethi, Ahmed El Youssefi, Yousef Farhaoui, Ahmad El Allaoui

Managing emergency vehicles efficiently is critical in urban areas where traffic jams and unpredictable road conditions can delay response times and put lives at risk. Over the years, machine learning methods like k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), combined with features like HOG and SIFT, paved the way for early image classification and object detection breakthroughs. Tools like Genetic Algorithms (GA) helped refine feature selection, while methods like AdaBoost and Random Forests improved decision-making reliability. The introduction of deep learning has transformed these systems. Convolutional Neural Networks (CNNs) now drive accurate emergency vehicle detection, while Siamese networks support precise identification, such as distinguishing between types of emergency vehicles. Attention mechanisms and Vision Transformers (ViTs) have enhanced the ability to understand context and handle complex scenarios, making them ideal for busy urban environments. Generative Adversarial Networks (GANs) tackle one of the biggest challenges in this field—limited training data—by creating realistic synthetic datasets. This review highlights how these advancements shape emergency response systems, from detecting emergency vehicles in real time to optimizing fleet management. It also explores the challenges of scaling these solutions and achieving faster processing speeds, providing a roadmap for researchers aiming to advance emergency vehicle technologies.

在城市地区，有效管理应急车辆至关重要，因为交通拥堵和不可预测的道路状况可能会延迟响应时间，危及生命。多年来，像k-近邻（k-NN）和支持向量机（SVM）这样的机器学习方法，结合HOG和SIFT等特征，为早期的图像分类和目标检测突破铺平了道路。遗传算法（GA）等工具有助于改进特征选择，而AdaBoost和Random Forests等方法则提高了决策的可靠性。深度学习的引入改变了这些系统。卷积神经网络（cnn）现在驱动准确的紧急车辆检测，而暹罗网络支持精确识别，例如区分紧急车辆的类型。注意力机制和视觉变形器（vit）增强了理解上下文和处理复杂场景的能力，使它们成为繁忙的城市环境的理想选择。生成对抗网络（GANs）通过创建真实的合成数据集来解决该领域最大的挑战之一——有限的训练数据。这篇综述强调了这些进步如何塑造应急响应系统，从实时检测应急车辆到优化车队管理。它还探讨了扩展这些解决方案和实现更快处理速度的挑战，为旨在推进应急车辆技术的研究人员提供了路线图。

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引用次数: 0

Improving long-term prediction in industrial processes using neural networks with noise-added training data 利用带有噪声的训练数据的神经网络改进工业过程的长期预测

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.iswa.2025.200579

Mohammadhossein Ghadimi Mahanipoor , Amirhossein Fathi

Accurate long-term prediction in industrial processes is essential for efficient control and operation. This study investigates the use of artificial neural networks (ANNs) for forecasting temperature in complex thermal systems, with a focus on enhancing model robustness under real-world conditions. A key innovation in this work is the intentional introduction of Gaussian noise into the training data to emulate sensor inaccuracies and environmental uncertainties, thereby improving the network's generalization capability. The target application is the prediction of water temperature in a non-stirred reservoir heated by two electric heaters, where phase change, thermal gradients, and sensor placement introduce significant modeling challenges. The proposed feedforward neural network architecture, comprising 90 neurons across three hidden layers, demonstrated a substantial reduction in long-term prediction error from 11.23 % to 2.02 % when trained with noise-augmented data. This result highlights the effectiveness of noise injection as a regularization strategy for improving performance in forecasting tasks. The study further contrasts this approach with Random Forest model and confirms the superior generalization and stability of the noise-trained ANN. These findings establish a scalable methodology for improving predictive accuracy in industrial systems characterized by limited data, strong nonlinearities, and uncertain measurements.

在工业过程中，准确的长期预测对于有效的控制和操作至关重要。本研究探讨了在复杂热系统中使用人工神经网络（ANNs）来预测温度，重点是增强模型在现实条件下的鲁棒性。这项工作的一个关键创新是有意在训练数据中引入高斯噪声来模拟传感器的不准确性和环境的不确定性，从而提高网络的泛化能力。目标应用是预测由两个电加热器加热的非搅拌储层中的水温，其中相位变化、热梯度和传感器放置带来了重大的建模挑战。所提出的前馈神经网络架构由三个隐藏层的90个神经元组成，当使用噪声增强数据训练时，长期预测误差从11.23%大幅降低到2.02%。这一结果突出了噪声注入作为一种改进预测任务性能的正则化策略的有效性。研究进一步将该方法与随机森林模型进行了对比，证实了噪声训练的人工神经网络具有优越的泛化和稳定性。这些发现建立了一种可扩展的方法，用于提高工业系统中有限数据、强非线性和不确定测量的预测精度。

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引用次数: 0

Indirect visual odometry with a light-field camera 使用光场摄像机的间接视觉里程计

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-10-31 DOI: 10.1016/j.iswa.2025.200600

Mohamad Al Assaad, Stéphane Bazeille, Christophe Cudel

Visual odometry is the technique of determining a robot’s pose by analyzing images of its surroundings as it moves. Visual odometry can be categorized into monocular when using a single camera, or stereo when using two cameras or more. In this study, we investigate the use of light-field camera for visual odometry. Capitalizing on the distinctive capability of a light-field camera to record both the intensity and the direction of light, we propose an indirect visual odometry method able to estimate the scale of the translation similarly to stereo visual odometry, but using a single camera sensor. Our visual odometry framework combines light-field imaging with conventional odometry techniques to track the camera movements, using the depth insights provided by a light-field depth estimation approach. Additionally, this method differs from state-of-the-art methods by using a simplified calibration process and a new keypoints extraction method, which makes the use of the light-field cameras easier for robotics perception.

视觉里程计是一种通过分析机器人运动时周围环境的图像来确定机器人姿势的技术。视觉里程计可以分为单目，当使用一个相机，或立体，当使用两个或更多的相机。在这项研究中，我们探讨了使用光场相机的视觉里程计。利用光场相机记录光的强度和方向的独特能力，我们提出了一种间接视觉里程计方法，能够估计平移的规模，类似于立体视觉里程计，但使用单个相机传感器。我们的视觉里程计框架将光场成像与传统的里程计技术相结合，利用光场深度估计方法提供的深度洞察来跟踪相机运动。此外，该方法与最先进的方法不同，它使用了简化的校准过程和新的关键点提取方法，这使得光场相机的使用更容易用于机器人感知。

引用次数: 0