Intelligent Systems with Applications最新文献_第2页

Vision transformers in precision agriculture: A comprehensive survey 精准农业中的视觉变压器：综合调查

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-13 DOI: 10.1016/j.iswa.2025.200617

Saber Mehdipour , Seyed Abolghasem Mirroshandel , Seyed Amirhossein Tabatabaei

Detecting plant diseases is a crucial aspect of modern agriculture, playing a key role in maintaining crop health and ensuring sustainable yields. Traditional approaches, though still valuable, often rely on manual inspection or conventional machine learning (ML) techniques, both of which face limitations in scalability and accuracy. The emergence of Vision Transformers (ViTs) marks a significant shift in this landscape by enabling superior modeling of long-range dependencies and offering improved scalability for complex visual tasks. This survey provides a rigorous and structured analysis of impactful studies that employ ViT-based models, along with a comprehensive categorization of existing research. It also offers a quantitative synthesis of reported performance — with accuracies ranging from 75.00% to 100.00% — highlighting clear trends in model effectiveness and identifying consistently high-performing architectures. In addition, this study examines the inductive biases of CNNs and ViTs, which is the first analysis of these architectural priors within an agricultural context. Further contributions include a comparative taxonomy of prior studies, an evaluation of dataset limitations and metric inconsistencies, and a statistical assessment of model efficiency across diverse crop-image sources. Collectively, these efforts clarify the current state of the field, identify critical research gaps, and outline key challenges — such as data diversity, interpretability, computational cost, and field adaptability — that must be addressed to advance the practical deployment of ViT technologies in precision agriculture.

植物病害检测是现代农业的一个重要方面，在保持作物健康和确保可持续产量方面发挥着关键作用。传统方法虽然仍然有价值，但通常依赖于人工检查或传统的机器学习（ML）技术，这两种方法在可扩展性和准确性方面都存在局限性。视觉转换器（vit）的出现标志着这一领域的重大转变，它支持远程依赖关系的高级建模，并为复杂的视觉任务提供改进的可伸缩性。本调查提供了一个严谨的和结构化的分析，有影响力的研究，采用基于虚拟现实的模型，以及现有研究的全面分类。它还提供了报告性能的定量综合——准确度范围从75.00%到100.00%——突出了模型有效性的清晰趋势，并确定了始终如一的高性能架构。此外，本研究考察了cnn和vit的归纳偏差，这是在农业背景下对这些建筑先验的首次分析。进一步的贡献包括对先前研究的比较分类，对数据集局限性和度量不一致性的评估，以及对不同作物图像来源的模型效率的统计评估。总的来说，这些努力澄清了该领域的现状，确定了关键的研究差距，并概述了关键挑战——例如数据多样性、可解释性、计算成本和现场适应性——必须解决这些问题，以推进ViT技术在精准农业中的实际部署。

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引用次数: 0

Enhancing token boundary detection in disfluent speech 非流利语音中token边界检测的增强

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-06 DOI: 10.1016/j.iswa.2025.200614

Manu Srivastava , Marcello Ferro , Vito Pirrelli , Gianpaolo Coro

This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.

本文提出了一种开源的自动语音识别（ASR）管道，该管道针对不流畅的意大利语读语音进行了优化，旨在提高低资源设置下的转录精度和令牌边界精度。本研究旨在解决传统的ASR系统在捕捉非流利阅读的时间不规则性方面所面临的困难，这对于流利性的心理语言学和临床分析至关重要。在WhisperX框架的基础上，该系统用基于能量的分割算法取代了神经语音活动检测模块，该算法旨在保留停顿和犹豫等韵律线索。双对齐策略集成了两个互补的音素级ASR模型来纠正初始偏移不对称，而偏置补偿后处理步骤则减轻了系统时序误差。对READLET（儿童读语）和CLIPS（成人读语）语料库的评估显示，与基线系统相比，该语料库具有一致性的改进，证实了在非流畅条件下边界检测和转录的鲁棒性增强。结果表明，所提出的体系结构为精确对齐和不流畅感知ASR提供了一个通用的、与语言无关的框架。该方法可以支持阅读流畅性和言语规划的下游分析，为计算语言学和临床言语研究做出贡献。

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引用次数: 0

A systematic review of vision transformer and explainable AI advances in multimodal facial expression recognition 系统回顾了视觉转换器和可解释人工智能在多模态面部表情识别中的进展

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-06 DOI: 10.1016/j.iswa.2025.200615

Ilya Kus , Cemal Kocak , Ayse Keles

Facial expression is one of the most important indicators used to convey human emotions. Facial expression recognition is the process of automatically detecting and classifying these expressions by computer systems. Multimodal facial expression recognition aims to perform a more accurate and comprehensive emotion analysis by combining facial expressions with different modalities such as image, speech, Electroencephalogram (EEG), or text. This study systematically reviews research conducted between 2021 and 2025 on the Vision Transformer (ViT) based approaches and Explainable Artificial Intelligence (XAI) techniques in multimodal facial expression recognition, as well as the datasets employed in these studies. The findings indicate that ViT-based models outperform conventional Convolutional Neural Networks (CNNs) by effectively capturing long-range dependencies between spatially distant facial regions, thereby enhancing emotion classification accuracy. However, significant challenges remain, including data privacy risks arising from the collection of multimodal biometric information, data imbalance and inter-modality incompatibility, high computational costs hindering real-time applications, and limited progress in model explainability. Overall, this study highlights that integrating advanced ViT architectures with robust XAI and privacy-preserving techniques can enhance the reliability, transparency, and ethical deployment of multimodal facial expression recognition systems.

面部表情是用来传达人类情感的最重要的指标之一。面部表情识别是计算机系统对面部表情进行自动检测和分类的过程。多模态面部表情识别旨在通过将面部表情与图像、语音、脑电图（EEG）或文本等不同模态相结合，进行更准确、更全面的情绪分析。本研究系统回顾了2021年至2025年间在多模态面部表情识别中基于视觉变形（ViT）的方法和可解释人工智能（XAI）技术的研究，以及这些研究中使用的数据集。研究结果表明，基于vit的模型可以有效地捕获空间距离较远的面部区域之间的远程依赖关系，从而提高情绪分类的准确性，从而优于传统的卷积神经网络（cnn）。然而，重大挑战仍然存在，包括多模态生物特征信息收集带来的数据隐私风险、数据不平衡和模态间不兼容、阻碍实时应用的高计算成本，以及模型可解释性方面的有限进展。总之，本研究强调，将先进的ViT架构与强大的XAI和隐私保护技术相结合，可以提高多模态面部表情识别系统的可靠性、透明度和道德部署。

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引用次数: 0

AL-ViT: Label-efficient Robusta coffee-bean defect detection in Thailand using active learning vision transformers AL-ViT：标签高效罗布斯塔咖啡豆缺陷检测在泰国使用主动学习视觉变压器

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-28 DOI: 10.1016/j.iswa.2025.200612

Sirawich Vachmanus , Wimolsiri Pridasawas , Worapan Kusakunniran , Kitti Thamrongaphichartkul , Noppanan Phinklao

In major training and export markets, the coffee bean grading process still relies heavily on manual labor to sort individual beans from large harvest volumes. This labor-intensive task is time-consuming, costly, and prone to human error, especially within Thailand’s rapidly expanding Robusta coffee sector. This study introduces AL–ViT, an end-to-end Active-Learning Vision Transformer framework that operationalizes active learning and transformer-based feature extraction within a single, production-oriented pipeline. The framework integrates a ViT-Base/16 backbone with seven active learning (AL) query strategies, random sampling, entropy-based selection, Bayesian Active Learning by Disagreement (BALD), Batch Active Learning by Diverse Gradient Embeddings (BADGE), Core-Set diversity sampling, ensemble disagreement, and a novel hybrid uncertainty–diversity strategy designed to balance informativeness and representativeness during sample acquisition. A high-resolution dataset of 2098 Robusta coffee bean images was collected under controlled-lighting conditions aligned with grading-machine setups, with only 5 % initially labeled and the remainder forming the AL pool. Across five random seeds, the hybrid strategy without MixUp augmentation achieved 97.1 % accuracy and an F1bad of 0.956 using just 850 labels (41 % of the dataset), within 0.3 percentage points of full supervision. Operational reliability, defined as 95 % accuracy, consistent with prior inspection benchmarks, was reached with only 407 labels, reflecting a 75 % reduction in annotation. Entropy sampling showed the fastest early-stage gains, whereas BADGE lagged by >1 pp; Core-Set and Ensemble provided moderate but stable results. Augmentation and calibration analyses indicated that explicit methods (MixUp, CutMix, RandAugment) offered no further benefit, with the hybrid pipeline already achieving well-calibrated probabilities. Statistical validation via paired t-tests, effect sizes, and bootstrap CIs confirmed consistent improvements of uncertainty-driven strategies over random sampling. Overall, the proposed AL–ViT framework establishes a label-efficient and practically deployable approach for agricultural quality control, achieving near-supervised accuracy at a fraction of the labeling cost.

在主要的培训和出口市场，咖啡豆分级过程仍然严重依赖人工从大量收获的咖啡豆中对单个咖啡豆进行分类。这项劳动密集型任务耗时、成本高昂，而且容易出现人为错误，尤其是在泰国迅速扩张的罗布斯塔咖啡行业。本研究介绍了AL-ViT，这是一个端到端的主动学习视觉转换器框架，可在单个面向生产的管道中实现主动学习和基于转换器的特征提取。该框架将ViT-Base/16主干与7种主动学习（AL）查询策略集成在一起：随机抽样、基于熵的选择、基于分歧的贝叶斯主动学习（BALD）、基于不同梯度嵌入的批量主动学习（BADGE）、核心集多样性抽样、集成分歧，以及一种新的混合不确定性多样性策略，旨在平衡样本获取过程中的信息性和代表性。在与分级机设置一致的受控照明条件下，收集了2098个罗布斯塔咖啡豆图像的高分辨率数据集，最初只有5%被标记，其余的形成人工智能池。在五个随机种子中，没有MixUp增强的混合策略仅使用850个标签（占数据集的41%）就实现了97.1%的准确率和0.956的F1bad，在0.3个百分点的完全监督范围内。操作可靠性，定义为95%的准确性，与先前的检查基准一致，仅达到407个标签，反映了75%的注释减少。熵采样显示了最快的早期收益，而BADGE滞后了1 pp；Core-Set和Ensemble提供了中等但稳定的结果。增强和校准分析表明，显式方法（MixUp, CutMix, RandAugment）没有进一步的好处，混合管道已经获得了很好的校准概率。通过配对t检验、效应大小和自举ci进行的统计验证证实，不确定性驱动策略比随机抽样有一致性的改进。总体而言，拟议的AL-ViT框架为农业质量控制建立了一种高效的标签和实际可部署的方法，以一小部分标签成本实现了近乎监督的准确性。

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引用次数: 0

Multi-modal document classification in AEC asset management AEC资产管理中的多模态文档分类

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-19 DOI: 10.1016/j.iswa.2025.200609

Floor Rademaker , Faizan Ahmed , Marcos R. Machado

The digitalization of asset management within the architecture, engineering and construction (AEC) sector is in need of effective methods for the automatic classification of documents. This study focuses on the development and evaluation of multimodal document classification models, utilizing visual, textual, and layout-related document information. We examine various state-of-the-art machine learning models and combine them through an iterative development process. The performance of these models is evaluated on two different AEC-document datasets. The results demonstrate that each of the modalities is useful in classifying the documents, as well as the integration of the different information types. This study contributes by applying AI techniques, specifically document classification in the AEC sector, setting the initial step to automating information extraction and processing for Intelligent Asset Management, and lastly, by combining and comparing multimodal state-of-the-art classification models on real-life datasets.

建筑、工程和建设（AEC）部门资产管理的数字化需要有效的文件自动分类方法。本研究的重点是开发和评估多模态文档分类模型，利用视觉、文本和布局相关的文档信息。我们研究了各种最先进的机器学习模型，并通过迭代开发过程将它们结合起来。在两个不同的aec文档数据集上评估了这些模型的性能。结果表明，每种模式对文档分类以及不同信息类型的集成都是有用的。本研究通过应用人工智能技术，特别是AEC领域的文档分类，为智能资产管理的自动化信息提取和处理设置了第一步，最后，通过结合和比较现实数据集上的多模态最新分类模型，做出了贡献。

引用次数: 0

AIOps for log anomaly detection in the era of LLMs: A systematic literature review llm时代日志异常检测的AIOps：系统的文献综述

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-19 DOI: 10.1016/j.iswa.2025.200608

Miguel De la Cruz Cabello , Tiago Prince Sales , Marcos R. Machado

Modern IT systems generate large volumes of log data that challenge timely and effective anomaly detection. Traditional methods often require intensive feature engineering and struggle to adapt to dynamic operational environments. This Systematic Literature Review (SLR) analyzes how Artificial Intelligence for IT Operations (AIOps) benefits from advanced language models, emphasizing Large Language Models (LLMs) for more effective log anomaly detection. By comparing state-of-art frameworks with LLM-driven methods, this study reveals that prompt engineering – the practice of designing and refining inputs to AI models to produce accurate and useful outputs – and Retrieval Augmented Generation (RAG) boost accuracy and interpretability without extensive fine-tuning. Experimental findings demonstrate that LLM-based approaches significantly outperform traditional methods across evaluation metrics that include F1-score, precision, and recall. Furthermore, the integration of LLMs with RAG techniques has shown a strong adaptability to changing environments. The applicability of these methods also extends to the military industry. Consequently, the development of specialized LLM systems with RAG tailored for the military industry represents a promising research direction to improve operational effectiveness and responsiveness of defense systems.

现代IT系统产生大量的日志数据，这对及时有效的异常检测提出了挑战。传统的方法通常需要密集的特征工程，并且难以适应动态的操作环境。这篇系统性文献综述（SLR）分析了IT运营人工智能（AIOps）如何从高级语言模型中受益，强调了大型语言模型（llm）可以更有效地检测日志异常。通过比较最先进的框架与法学硕士驱动的方法，本研究表明，快速工程（设计和改进人工智能模型输入的实践，以产生准确和有用的输出）和检索增强生成（RAG）提高了准确性和可解释性，而无需进行大量微调。实验结果表明，基于法学硕士的方法在评估指标（包括f1分数、精度和召回率）上明显优于传统方法。此外，法学硕士与RAG技术的集成显示出对不断变化的环境的强大适应性。这些方法的适用性也延伸到军事工业。因此，为军事工业量身定制具有RAG的专用LLM系统的开发代表了一个有前途的研究方向，可以提高国防系统的作战效率和响应能力。

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引用次数: 0

Musipainter: A music-conditioned generative architecture for artistic image synthesis Musipainter：一种以音乐为条件的艺术图像合成生成建筑

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-19 DOI: 10.1016/j.iswa.2025.200611

Alfredo Baione , Giuseppe Rizzo , Luca Barco , Angelica Urbanelli , Luigi Di Biasi , Genoveffa Tortora

Generative art is a challenging area of research in deep generative modeling. Exploring AI’s role in human–machine co-creative processes requires understanding machine learning’s potential in the arts. Building on this premise, this paper presents Musipainter, a cross-modal generative framework adapted to create artistic images that are historically and stylistically aligned with 30-second musical inputs, with a focus on creative and semantic coherence. To support this goal, we introduce Museart, a dataset designed explicitly for this research, and GIILS, a creativity-oriented metric that enables us to assess both artistic-semantic consistency and diversity in the generated outputs. The results indicate that Musipainter, supported by the Museart dataset and the exploratory GIILS metric, can offer a foundation for further research on AI’s role in artistic generation, while also highlighting the need for systematic validation and future refinements.

生成艺术是深度生成建模中一个具有挑战性的研究领域。探索人工智能在人机共同创造过程中的作用需要理解机器学习在艺术中的潜力。在此前提下，本文介绍了Musipainter，这是一个跨模态生成框架，用于创建与30秒音乐输入在历史和风格上一致的艺术图像，重点是创造性和语义一致性。为了实现这一目标，我们引入了专门为本研究设计的数据集Museart和GIILS，这是一个以创造力为导向的度量标准，使我们能够评估生成输出中的艺术语义一致性和多样性。结果表明，在Museart数据集和探索性GIILS指标的支持下，Musipainter可以为进一步研究人工智能在艺术生成中的作用提供基础，同时也强调了系统验证和未来改进的必要性。

引用次数: 0

A corporate credit evaluation method considering strong feature privacy with non-private label: A vertical heterogeneous feature fusion approach 一种考虑非私有标签强特征隐私的企业信用评价方法：垂直异构特征融合方法

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-13 DOI: 10.1016/j.iswa.2025.200603

Xifeng Ning , Chao Yang , Hailu Sun , Xinyuan Song , Zifan Hu , Yu Feng , Jiawei Li , Yifan Zhu

In modern monitoring and operational management, whether in industrial systems, financial risk control, or infrastructure maintenance, decision-making increasingly relies on integrating heterogeneous data from multiple sources. However, due to data privacy regulations, distributed storage, communication constraints, and sensor failures, it is often difficult to centralize modeling when dealing with high-dimensional, incomplete datasets held by different institutions. Federated learning offers a privacy-preserving joint modeling solution, yet still faces challenges such as high communication overhead, low robustness to participant dropout, and risks of gradient leakage. In certain incomplete-data scenarios, not all data is private—labels such as equipment inspection results, fault reports, or corporate blacklists and whitelists published by authoritative bodies may be public—while feature data remains private and partially missing. To address this, we propose an innovative collaborative modeling framework tailored for incomplete-data monitoring and operations, in which each participant independently trains a model on its private features and exchanges only prediction results rather than gradients. Inspired by collective expert scoring, each “expert” evaluates based on its own data, then shares scores that are integrated into a comprehensive assessment. This approach offers multiple advantages: independent model training for each party, improved efficiency by migrating only prediction results, enhanced security by avoiding gradient transmission, and higher robustness since the failure of one participant does not halt others’ training. We present three variants of this prediction-result fusion method and evaluate them on representative datasets, including enterprise credit risk assessment as a case study, comparing against vertical federated logistic regression. Experimental results validate the effectiveness of the proposed approach, which can be widely applied to diverse monitoring and operational scenarios under incomplete data conditions.

在现代监控和运营管理中，无论是工业系统、金融风险控制还是基础设施维护，决策越来越依赖于对多源异构数据的集成。然而，由于数据隐私法规、分布式存储、通信约束和传感器故障，在处理不同机构持有的高维、不完整数据集时，通常很难集中建模。联邦学习提供了一种保护隐私的联合建模解决方案，但仍然面临着诸如高通信开销、参与者退出的低鲁棒性以及梯度泄漏风险等挑战。在某些数据不完整的场景中，并非所有数据都是私有数据，例如权威机构发布的设备检查结果、故障报告或企业黑名单和白名单可能是公开的，而特征数据仍然是私有的，部分缺失。为了解决这个问题，我们提出了一个创新的协作建模框架，为不完整的数据监测和操作量身定制，其中每个参与者根据其私有特征独立训练模型，并且只交换预测结果而不是梯度。受集体专家评分的启发，每个“专家”根据自己的数据进行评估，然后分享分数，这些分数被整合到一个综合评估中。这种方法具有多种优势：对每一方进行独立的模型训练，通过只迁移预测结果提高效率，通过避免梯度传输增强安全性，并且由于一个参与者的失败不会停止其他参与者的训练，因此具有更高的鲁棒性。我们提出了这种预测-结果融合方法的三种变体，并在代表性数据集上对它们进行了评估，其中包括以企业信用风险评估为例的研究，并与垂直联邦逻辑回归进行了比较。实验结果验证了该方法的有效性，可广泛应用于不完全数据条件下的各种监测和操作场景。

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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 : 2025-11-13 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

Optimizing printing processes with MCTS 使用MCTS优化打印过程

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-10 DOI: 10.1016/j.iswa.2025.200602

Kadri Kukk , Ants Torim , Erki Eessaar , Tarmo Kadak

The printing industry benefits from digitalizing workflows such as customer quoting. Intelligent printing process planning is essential to determine the near-optimal price for automated quoting. This paper addresses the automation of sheet imposition, a critical and computationally intensive step in optimizing the printing process that belongs to the general class of cutting and packing problems. We propose a simple recursive sheet imposition representation as the basis for our algorithms. The Brute Force algorithm for optimizing sheet imposition guarantees the cheapest solution but is computationally infeasible for complex tasks. As alternatives, we investigate heuristic algorithms, specifically Monte Carlo Tree Search (MCTS) and Simulated Annealing (SA). Our findings show that while Brute Force is prohibitively slow, MCTS strikes a robust balance between computational performance and solution quality, consistently finding solutions within a 5% margin of optimal price. Although SA can occasionally find superior solutions, MCTS provides a more reliable and efficient approach by consistently delivering results close to the optimal price.

印刷行业受益于数字化工作流程，如客户报价。智能印刷工艺规划对于确定近乎最优的自动报价价格至关重要。本文讨论了纸张拼版的自动化，这是优化印刷过程的一个关键和计算密集的步骤，属于一般的切割和包装问题。我们提出了一个简单的递归拼版表示作为我们算法的基础。蛮力算法用于优化板材拼装保证了最便宜的解决方案，但计算上不可行的复杂任务。作为替代方案，我们研究了启发式算法，特别是蒙特卡罗树搜索（MCTS）和模拟退火（SA）。我们的研究结果表明，虽然蛮力算法速度非常慢，但MCTS在计算性能和解决方案质量之间取得了良好的平衡，始终在最优价格的5%范围内找到解决方案。虽然SA偶尔可以找到更好的解决方案，但MCTS提供了一种更可靠、更有效的方法，它始终如一地提供接近最优价格的结果。

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引用次数: 0