Expert Systems with Applications最新文献_第5页

Evolutionary constrained optimization based on causal random forest 基于因果随机森林的进化约束优化

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-09 DOI: 10.1016/j.eswa.2026.131417

Yinghan Hong , Sirui Liang , Jiahao Lian , Guizhen Mai , Liang Zhao , Yueting Xu , Yi Xiang , Hao Zhang , Fangqing Liu , Zhifeng Hao

Constrained optimization problems constitute a class of optimization tasks that aim to maximize or minimize an objective function subject to intricate constraints. Evolutionary algorithms are extensively employed to tackle these problems, but the inherent nonlinearity, discontinuity, and restricted feasible regions of constrained optimization problems present significant challenges, and existing approaches often rely on predefined rules or empirical thresholds, which limit their adaptability and hinder causal interpretability. To overcome this limitation, this study proposes an evolutionary constrained optimization algorithm based on causal random forest that leverages causal random forest to quantify the causal strength between objective optimization and constraint satisfaction, thereby guiding the evolutionary search in a principled and informed manner. Furthermore, a dynamic adaptive strategy-switching mechanism is incorporated into the algorithm to reduce the reliance on empirical thresholds and fixed rules, which enhances the self-adaptive capability of the algorithm under complex and sophisticated constraints. Extensive experimental results on the CEC2006, CEC2010, and CEC2017 benchmark suites demonstrate that the proposed method consistently outperforms existing methods, underscoring its effectiveness and robustness in handling constrained optimization problems.

约束优化问题是一类优化任务，其目的是在复杂的约束条件下最大化或最小化目标函数。进化算法被广泛用于解决这些问题，但约束优化问题固有的非线性、不连续和受限可行区域提出了重大挑战，现有方法通常依赖于预定义的规则或经验阈值，这限制了它们的适应性并阻碍了因果可解释性。为了克服这一局限性，本研究提出了一种基于因果随机森林的进化约束优化算法，利用因果随机森林量化目标优化与约束满足之间的因果强度，从而有原则、有信息地指导进化搜索。在算法中引入动态自适应策略切换机制，减少了对经验阈值和固定规则的依赖，增强了算法在复杂约束条件下的自适应能力。在CEC2006、CEC2010和CEC2017基准套件上的大量实验结果表明，该方法始终优于现有方法，强调了其在处理约束优化问题方面的有效性和鲁棒性。

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引用次数: 0

Using mobile charging stations as probes to discover latent EV charging demand in stochastic environments: A deep reinforcement learning approach 基于移动充电站的随机环境下电动汽车潜在充电需求挖掘：深度强化学习方法

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-02 DOI: 10.1016/j.eswa.2026.131433

Atefeh Hemmati Golsefidi , Frederik Boe Hüttel , Samitha Samaranayake , Francisco Câmara Pereira

The increasing adoption of electric vehicles underscores the urgent need for efficient and reliable charging infrastructure. Fixed charging stations are crucial for meeting surging demand and ensuring convenient access, yet accurately predicting demand remains highly challenging, because charging patterns change in response to where stations are placed, which creates a cyclical dilemma for planning. However, mobile charging stations (MCSs) offer a novel solution by flexibly relocating across urban areas, they can both deliver energy and act as dynamic probes to collect real-time data on charging demand. Existing studies, however, typically assume prior knowledge of demand distributions, which is rarely available in emerging EV markets or where privacy concerns limit data access. This paper proposes a Deep Reinforcement Learning (DRL) approach, formulated as a Partially Observable Markov Decision Process (POMDP), to optimize the relocation of MCSs in conjunction with fixed charging stations, while simultaneously uncovering latent demand patterns. We employ an Advantage Actor-Critic (A2C) algorithm with Long Short-Term Memory (LSTM) networks to capture temporal dependencies and adapt to stochastic demand. A dynamic Mixed-Integer Programming (MIP) model is developed as a benchmark that represents an idealized case with perfect foresight of demand. We compare the DRL agent against this optimization model in two settings: (i) a synthetic toy environment for controlled testing, and (ii) a realistic simulation of the Frederiksberg municipality in Denmark, calibrated with real charging data. The results show that the DRL framework effectively adapts to stochastic demand, outperforms the optimization baseline under uncertainty, and scales efficiently to larger problem instances. Beyond methodological contributions, the findings highlight how MCSs can serve a dual role as infrastructure supplements and as demand-discovery tools, offering valuable insights for data-driven and adaptive EV charging planning.

电动汽车的日益普及凸显了对高效可靠的充电基础设施的迫切需求。固定充电站对于满足激增的需求和确保方便使用至关重要，但准确预测需求仍然极具挑战性，因为充电模式会随着充电站的位置而变化，这给规划带来了周期性困境。然而，移动充电站（mcs）提供了一种新颖的解决方案，通过在城市地区灵活地迁移，它们既可以提供能量，又可以作为动态探测器收集充电需求的实时数据。然而，现有的研究通常假设需求分布的先验知识，这在新兴的电动汽车市场或隐私问题限制数据访问的地方很少可用。本文提出了一种深度强化学习（DRL）方法，将其描述为部分可观察马尔可夫决策过程（POMDP），以优化mcs与固定充电站的搬迁，同时发现潜在的需求模式。我们采用了一种具有长短期记忆（LSTM）网络的优势行为-批判（A2C）算法来捕捉时间依赖性并适应随机需求。建立了一个动态混合整数规划（MIP）模型作为基准，该模型代表了对需求具有完全预见的理想情况。我们将DRL代理与该优化模型在两种设置下进行比较：(i)用于控制测试的合成玩具环境，以及（ii）丹麦腓特烈斯堡市的现实模拟，使用真实充电数据进行校准。结果表明，该框架能有效地适应随机需求，在不确定情况下优于优化基线，并能有效地扩展到更大的问题实例。除了方法上的贡献外，研究结果还强调了mcs如何作为基础设施补充和需求发现工具的双重作用，为数据驱动和自适应电动汽车充电规划提供了有价值的见解。

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引用次数: 0

AnomalyLVM:Vision-language models for zero-shot anomaly detection AnomalyLVM：零射击异常检测的视觉语言模型

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-02 DOI: 10.1016/j.eswa.2026.131392

Yuqing Zhao, Min Meng, Jigang Wu

Zero-Shot Anomaly Detection (ZSAD) has emerged as a promising approach for identifying unseen defects without requiring annotated training samples, but existing methods typically focus only on image-level detection and overlook fine-grained pixel-level localization. To address this gap, we propose AnomalyLVM, a unified vision-language framework designed to simultaneously handle image-level classification and pixel-level segmentation in zero-shot settings. AnomalyLVM leverages frozen SAM2 and DINO-X as dual visual encoders to extract complementary spatial and semantic features, which are fused and decoded via a lightweight decoder to generate localization maps. Meanwhile, a frozen CLIP text encoder guides image-level detection through semantic similarity matching. To enhance the accuracy of pixel-wise supervision, we introduce a Feature Enhancement Module (FEM) that dynamically refines static LayerCAM maps by integrating attention cues from both visual encoders and decoder affinity signals, resulting in more consistent and context-aware pseudo labels. Additionally, we adopt a prompt-free, object-agnostic strategy that replaces handcrafted templates with learnable, generic prompts, enabling AnomalyLVM to generalize across diverse categories and defect types without relying on domain-specific knowledge. Extensive experiments conducted across 17 real-world anomaly detection datasets from industrial and medical domains indicate that AnomalyLVM outperforms other ZSAD methods and can generalize better to different categories and even domains. Code will be made available at https://github.com/hanli6688/AnomalyLVM

零射击异常检测（Zero-Shot Anomaly Detection， ZSAD）已经成为一种很有前途的方法，可以在不需要带注释的训练样本的情况下识别看不见的缺陷，但现有的方法通常只关注图像级检测，而忽略了细粒度的像素级定位。为了解决这一差距，我们提出了一个统一的视觉语言框架AnomalyLVM，旨在同时处理零拍摄设置中的图像级分类和像素级分割。AnomalyLVM利用冻结的SAM2和DINO-X作为双视觉编码器来提取互补的空间和语义特征，这些特征通过轻量级解码器融合和解码以生成定位地图。同时，一个固定的CLIP文本编码器通过语义相似度匹配引导图像级检测。为了提高逐像素监督的准确性，我们引入了一个特征增强模块（FEM），该模块通过集成来自视觉编码器和解码器亲和力信号的注意线索来动态地细化静态LayerCAM地图，从而产生更一致和上下文感知的伪标签。另外，我们采用一种无提示的、对象不可知的策略，用可学习的、通用的提示代替手工制作的模板，使AnomalyLVM能够在不同的类别和缺陷类型之间泛化，而不依赖于特定领域的知识。在来自工业和医疗领域的17个真实世界异常检测数据集上进行的大量实验表明，AnomalyLVM优于其他ZSAD方法，可以更好地泛化到不同的类别甚至领域。代码将在https://github.com/hanli6688/AnomalyLVM上提供

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引用次数: 0

DSF-Unet: A dual-stream fusion denoising diffusion framework for imbalanced wafer defect classification DSF-Unet：用于不平衡晶圆缺陷分类的双流融合去噪扩散框架

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-08 DOI: 10.1016/j.eswa.2026.131471

Rongbin Xu , Zhiqiang Xu , Jixiang Wang , Ying Xie , Lijie Wen , Yun Yang

Wafer defect recognition is crucial for semiconductor manufacturing. However, its accuracy is often limited by severe imbalance among defect categories. To address this challenge, we propose a Dual-Stream Fusion Unet denoising diffusion framework (DSF-Unet), which synthesizes diverse and high-quality wafer samples for minority classes. DSF-Unet builds upon a Unet encoder-decoder backbone and introduces two complementary components. The Bidirectional Mamba (Bi-Mamba) module models long-range spatial dependencies through state-space dynamics, while the Adaptive multi-scale attention (Am-att) module enhances spatial feature representation via cross-channel calibration. By jointly capturing global contextual information and fine-grained local defect patterns, these two modules enable the generation of balanced and representative synthetic samples, thereby effectively alleviating data imbalance. For further performance improvement, an enhanced Channel-Spatial Residual Network (CS-ResNet) is introduced for classification, which embeds a dual channel-spatial attention mechanism into the ResNet backbone to recalibrate feature responses and highlight defect-relevant regions. Experiments on two benchmark datasets demonstrate that DSF-Unet achieves superior performance, reaching 95.12% accuracy on WM-811K and 98.25% on Mixed-WM38.

晶圆缺陷识别在半导体制造中至关重要。然而，其准确性经常受到缺陷类别之间严重不平衡的限制。为了解决这一挑战，我们提出了一种双流融合Unet去噪扩散框架（DSF-Unet），它为少数族裔合成了多样化和高质量的晶圆样本。DSF-Unet建立在Unet编码器-解码器主干上，并引入两个互补的组件。双向曼巴（Bi-Mamba）模块通过状态-空间动态建模远程空间依赖性，而自适应多尺度注意（Am-att）模块通过跨通道校准增强空间特征表示。通过联合捕获全局上下文信息和细粒度的局部缺陷模式，这两个模块能够生成平衡的、具有代表性的合成样本，从而有效地缓解数据不平衡。为了进一步提高性能，引入了一种增强的通道-空间残差网络（CS-ResNet）进行分类，该网络在ResNet主干中嵌入了双通道-空间注意机制，以重新校准特征响应并突出缺陷相关区域。在两个基准数据集上的实验表明，DSF-Unet在WM-811K上的准确率达到95.12%，在mix - wm38上的准确率达到98.25%。

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引用次数: 0

Escaping from saddle points with perturbed gradient estimation 用扰动梯度估计从鞍点逃逸

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-11 DOI: 10.1016/j.eswa.2026.131549

Jingjing Chen , Sanyang Liu

For non-convex functions where derivative information is difficult to obtain, escaping saddle points remains a significant challenge. Existing zeroth-order optimization algorithms approximate the true gradient using unbiased gradient estimation techniques, employing zero-mean random perturbations, or exploring negative curvature directions to escape saddle points. However, these methods encounter near-zero approximate gradients in the vicinity of saddle points, necessitating multiple small perturbations to escape, thereby consuming a substantial number of function evaluations. In this work, we propose the Two-step Simultaneous Perturbation Stochastic Approximation (2-SPSA) approach, to facilitate saddle point escape, which requires fewer function evaluations. At each iteration, this method requires only 4 function evaluations to estimate the gradients at the current point and its neighboring point, of which their convex combination serves as the descent direction. The randomness inherent in this gradient estimation aids in rapidly jumping out of saddle points. Experimental results indicate that the proposed method can escape saddle points with fewer function evaluations compared to other zeroth-order optimization algorithms.

对于难以获得导数信息的非凸函数，转义鞍点仍然是一个重大挑战。现有的零阶优化算法使用无偏梯度估计技术近似真实梯度，采用零均值随机扰动，或探索负曲率方向以逃避鞍点。然而，这些方法在鞍点附近遇到接近于零的近似梯度，需要多个小的扰动才能逃脱，从而消耗大量的函数评估。在这项工作中，我们提出了两步同步摄动随机逼近（2-SPSA）方法，以促进鞍点逃逸，这需要更少的函数评估。在每次迭代中，该方法只需要4次函数求值来估计当前点及其相邻点的梯度，它们的凸组合作为下降方向。这种梯度估计固有的随机性有助于快速跳出鞍点。实验结果表明，与其他零阶优化算法相比，该方法能够以更少的函数求值逃避鞍点。

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引用次数: 0

Bayesian inference of nonlinear malaria dynamics in Ghana via an ensemble Markov chain Monte Carlo sampler 基于马尔可夫链蒙特卡罗采样器的加纳非线性疟疾动力学贝叶斯推断

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-06 DOI: 10.1016/j.eswa.2026.131540

T. Ansah-Narh , Y. Asare Afrane , J. Bremang Tandoh

Reliable quantification of malaria dynamics in sub-Saharan Africa remains hindered by short, noisy, and spatially heterogeneous surveillance records that challenge the assumptions of conventional deterministic models. In Ghana, health-facility data between 2014 and 2023 reveal highly non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture such stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that integrates a cubic deterministic baseline with a damped oscillatory kernel, estimated via an affine-invariant ensemble Markov Chain Monte Carlo sampler. The framework accommodates limited data, explicitly models parameter uncertainty, and generates probabilistic forecasts of malaria admissions for children under five years and individuals aged five years or more. Results demonstrate that the proposed hybrid cubic-damped oscillatory kernel model achieves strong empirical adequacy (

R^{2} = 0.9958

for  < 5 years;

R^{2} = 0.9956

for  ≥ 5 years) with residual errors below 2% and unimodal, well-mixed posterior distributions confirming robust convergence. District-level analysis reveals pronounced spatial heterogeneity, with coefficients of variation ranging from  < 0.07 in stable urban centres such as Kumasi to  > 3.3 in peripheral districts including Mpohor and Bia East. Forecasts for 2024–2026 indicate a gradual resurgence in admissions, increasing from approximately 137,000 to 149,000 cases among children under five and from 348,000 to 375,000 among older individuals, with uncertainty widening modestly over time. By producing interpretable probabilistic forecasts, this Bayesian framework provides a principled tool for anticipating short-term malaria fluctuations, guiding resource allocation, and strengthening data-driven decision-making within Ghana’s national malaria control strategy.

撒哈拉以南非洲疟疾动态的可靠量化仍然受到短期、嘈杂和空间异质性监测记录的阻碍，这些记录挑战了传统确定性模型的假设。在加纳，2014年至2023年期间的卫生设施数据显示，住院人数的波动高度非线性，且随年龄而异，但现有方法难以捕捉这种随机变异性，或提供可信的不确定性界限。本研究开发了一个贝叶斯非线性推理框架，该框架集成了三次确定性基线和阻尼振荡核，通过仿射不变系综马尔可夫链蒙特卡罗采样器估计。该框架可容纳有限的数据，明确模拟参数的不确定性，并生成5岁以下儿童和5岁或5岁以上个人疟疾入院的概率预测。结果表明，所提出的混合三阻尼振荡核模型具有较强的经验充分性（对于 <； 5年，R2=0.9958；对于 ≥ 5年，R2=0.9956），残差小于2%，单峰、混合良好的后验分布证实了鲁棒性收敛。区级分析显示出明显的空间异质性，变异系数从库马西等稳定城市中心的 <； 0.07到包括Mpohor和Bia East在内的外围地区的 >； 3.3不等。对2024-2026年的预测表明，入院人数逐渐回升，五岁以下儿童的病例从大约13.7万增加到14.9万，老年人的病例从34.8万增加到37.5万，不确定性随着时间的推移而适度扩大。通过产生可解释的概率预测，该贝叶斯框架为预测短期疟疾波动、指导资源分配和加强加纳国家疟疾控制战略中的数据驱动决策提供了一个原则性工具。

{"title":"Bayesian inference of nonlinear malaria dynamics in Ghana via an ensemble Markov chain Monte Carlo sampler","authors":"T. Ansah-Narh , Y. Asare Afrane , J. Bremang Tandoh","doi":"10.1016/j.eswa.2026.131540","DOIUrl":"10.1016/j.eswa.2026.131540","url":null,"abstract":"<div><div>Reliable quantification of malaria dynamics in sub-Saharan Africa remains hindered by short, noisy, and spatially heterogeneous surveillance records that challenge the assumptions of conventional deterministic models. In Ghana, health-facility data between 2014 and 2023 reveal highly non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture such stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that integrates a cubic deterministic baseline with a damped oscillatory kernel, estimated via an affine-invariant ensemble Markov Chain Monte Carlo sampler. The framework accommodates limited data, explicitly models parameter uncertainty, and generates probabilistic forecasts of malaria admissions for children under five years and individuals aged five years or more. Results demonstrate that the proposed hybrid cubic-damped oscillatory kernel model achieves strong empirical adequacy (<span><math><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.9958</mn></mrow></math></span> for  < 5 years; <span><math><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.9956</mn></mrow></math></span> for  ≥ 5 years) with residual errors below 2% and unimodal, well-mixed posterior distributions confirming robust convergence. District-level analysis reveals pronounced spatial heterogeneity, with coefficients of variation ranging from  < 0.07 in stable urban centres such as Kumasi to  > 3.3 in peripheral districts including Mpohor and Bia East. Forecasts for 2024–2026 indicate a gradual resurgence in admissions, increasing from approximately 137,000 to 149,000 cases among children under five and from 348,000 to 375,000 among older individuals, with uncertainty widening modestly over time. By producing interpretable probabilistic forecasts, this Bayesian framework provides a principled tool for anticipating short-term malaria fluctuations, guiding resource allocation, and strengthening data-driven decision-making within Ghana’s national malaria control strategy.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131540"},"PeriodicalIF":7.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid FMECA framework integrating fuzzy–Bayesian reasoning for modeling complex risk dependencies in safety-critical systems 基于模糊-贝叶斯推理的混合FMECA框架在安全关键系统中的复杂风险依赖建模

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-10 DOI: 10.1016/j.eswa.2026.131530

Akbar Rostamabadi , Esmaeil Zarei

Failure Modes, Effects, and Criticality Analysis (FMECA) is a widely used tool for risk and reliability assessment of engineering systems. However, its applicability to complex safety–critical systems is limited by several shortcomings, including the neglect of unknown failure causes, the focus on single failure modes, and the reliance on crisp numbers. This study proposes an Integrated Fuzzy–BWM–BN–FMECA (IFBBF) framework to model complex risk dependencies and address uncertainties in the failure analysis of complex safety–critical systems. The model consists of three steps. First, a qualitative analysis framework is developed based on the FMECA structure. Second, the qualitative framework is mapped into a Bayesian Network (BN) to model dependencies within cause–failure–effect chains and enable probabilistic updating. Advanced soft-computing techniques—including the Fuzzy Best–Worst Method (F-BWM), Noisy-OR (N-OR), and Leaky Noisy-OR (LN-OR) logics—are applied to reduce uncertainty and account for unknown failure causes. Third, a criticality analysis is performed using fuzzy BN reasoning, applying both predictive and diagnostic inferences. The proposed model is applied to the failure analysis of an industrial fire-tube boiler. Results indicated that the proposed approach significantly enhances the criticality assessment of failure modes compared to the conventional Risk Priority Number (RPN). It provides a comprehensive analysis of cascading effects and interdependencies among failure-causes-effect chains, enables probabilistic updating, and incorporates unknown failure causes— issues that conventional FMECA and other fuzzy-MCDM-based FMECA models cannot adequately address. The study findings demonstrate substantial improvements in the analytical capability of FMECA, offering a more flexible, detailed, and reliable framework for risk analysis of complex safety–critical systems.

失效模式、影响和临界性分析（FMECA）是一种广泛应用于工程系统风险和可靠性评估的工具。然而，它对复杂安全关键系统的适用性受到几个缺点的限制，包括忽视未知的故障原因，关注单一的故障模式，以及依赖于清晰的数字。本研究提出了一个集成的模糊- bwm - bn - fmeca （IFBBF）框架来建模复杂的风险依赖关系，并解决复杂安全关键系统失效分析中的不确定性。该模型包括三个步骤。首先，建立了基于FMECA结构的定性分析框架。其次，将定性框架映射到贝叶斯网络（BN）中，对因果关系链中的依赖关系进行建模，并实现概率更新。先进的软计算技术-包括模糊最佳-最差方法（F-BWM），噪声或（N-OR）和泄漏噪声或（LN-OR）逻辑-被用于减少不确定性和解释未知的故障原因。第三，使用模糊BN推理进行临界分析，应用预测和诊断推理。将该模型应用于某工业火管锅炉的失效分析。结果表明，与传统的风险优先级数（RPN）相比，该方法显著提高了失效模式的临界性评估。它提供了故障-因果链之间的级联效应和相互依赖性的综合分析，实现了概率更新，并纳入了未知的故障原因-传统FMECA和其他基于模糊mcdm的FMECA模型无法充分解决的问题。研究结果表明，FMECA的分析能力有了实质性的提高，为复杂安全关键系统的风险分析提供了更灵活、更详细和更可靠的框架。

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引用次数: 0

Adaptive bottleneck transformer for multimodal EEG, audio, and vision fusion 多模态脑电、音频和视觉融合的自适应瓶颈变压器

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-04 DOI: 10.1016/j.eswa.2026.131487

Sabina Bralina , Adnan Yazici , Cuntai Guan , Min-Ho Lee

Facial and speech expressions are primary cues for emotion recognition, while EEG provides a complementary neural perspective when external signals are ambiguous or absent. Although each modality contributes unique affective information, integrating such heterogeneous signals remains a major challenge in multimodal fusion research. To address this, the Adaptive Multimodal Bottleneck Transformer (AMBT) is introduced as a novel architecture, enabling efficient cross-modal interaction through adapters embedded within intermediate Transformer layers. These adapters 1) enhance stability by leveraging bottleneck tokens to prevent premature collapse, 2) enrich backbone representations while preserving unimodal capacity, 3) enable seamless integration across heterogeneous Transformer architectures, and 4) enable parameter-efficient training with fewer than 1% additional trainable parameters. AMBT was evaluated on three benchmark datasets: EAV (85.1%), CREMA-D (90.9%), and DEAP (98.7%), demonstrating competitive performance across all datasets. This results demonstrate the ability of AMBT to exploit complementary multimodal signals in a computationally efficient manner.

面部和言语表达是情绪识别的主要线索，而脑电图在外部信号模糊或缺失时提供了补充的神经视角。尽管每种情态都提供了独特的情感信息，但如何整合这种异质性信号仍然是多情态融合研究的主要挑战。为了解决这个问题，引入了自适应多模态瓶颈变压器（AMBT）作为一种新的体系结构，通过嵌入在中间变压器层中的适配器实现高效的跨模态交互。这些适配器1)通过利用瓶颈令牌来防止过早崩溃来增强稳定性，2)在保留单模态容量的同时丰富骨干表示，3)支持跨异构Transformer架构的无缝集成，以及4)使用少于1%的额外可训练参数实现参数高效训练。AMBT在三个基准数据集上进行了评估：EAV（85.1%）、CREMA-D（90.9%）和DEAP(98.7%)，在所有数据集上都表现出竞争力。这一结果证明了AMBT以计算效率高的方式利用互补多模态信号的能力。

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引用次数: 0

A digital twin-based approach for dynamic traffic-aware routing and charging of electric vehicles 基于数字孪生的电动汽车动态交通感知路径和充电方法

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-01-29 DOI: 10.1016/j.eswa.2026.131380

Yue Xie , Shanshan Li , Linjun Lu , Yuandong Pan , Fumiya Iida

The growing adoption of electric vehicles (EVs) presents new challenges for intelligent transportation systems (ITS), particularly in dynamic traffic environments where routing and charging decisions must adapt to fluctuating conditions. This paper proposes a Digital Twin-based Electric Vehicle Routing and Charging approach (DT-EVRC) that integrates real-time traffic data, predictive analytics, and a Dual-Population Evolutionary Algorithm (DPEA) to optimize EV travel and charging schedules. Unlike traditional static or simplified models, DT-EVRC continuously synchronizes with the physical transportation network, capturing variations in traffic density, charging station availability, and energy constraints. Experimental results on diverse grid-based urban scenarios demonstrate that DT-EVRC achieves robust and adaptive performance under traffic disruptions, road closures, and charging station failures. The proposed approach highlights the potential of digital twin technologies, combined with advanced optimization, to support next-generation ITS by enabling efficient, resilient, and sustainable urban mobility.

电动汽车（ev）的日益普及给智能交通系统（ITS）提出了新的挑战，特别是在动态交通环境中，路线和充电决策必须适应波动的条件。本文提出了一种基于数字孪生的电动汽车路径和充电方法（DT-EVRC），该方法集成了实时交通数据、预测分析和双种群进化算法（DPEA），以优化电动汽车的行驶和充电计划。与传统的静态或简化模型不同，DT-EVRC与物理交通网络持续同步，捕捉交通密度、充电站可用性和能源约束的变化。在多种基于电网的城市场景下的实验结果表明，DT-EVRC在交通中断、道路封闭和充电站故障情况下具有鲁棒性和自适应性能。提出的方法强调了数字孪生技术的潜力，结合先进的优化，通过实现高效、有弹性和可持续的城市交通，支持下一代智能交通系统。

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引用次数: 0

Doctors ranking based on PSO-BP model and attention mechanism with variable weights from the perspective of online medical consultation platforms 基于PSO-BP模型和变权关注机制的医生排名——基于在线医疗咨询平台的视角

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-05-25 Epub Date: 2026-02-07 DOI: 10.1016/j.eswa.2026.131349

Na Zhao , Zihao Zhang , Fengjun Liu , Zeshui Xu , Yongqing Yang

Current online medical consultation platforms face the challenge of low doctor engagement, significantly hindering the service quality and sustainable development. Existing doctor ranking mechanisms fail to effectively motivate doctors’ active participation and fully harness their contribution potential. Therefore, this paper proposes a novel doctor ranking method from the perspective of online medical consultation platforms to stimulate doctors’ engagement. Specifically, this paper first constructs a comprehensive doctor evaluation attribute system based on incomplete online data. Then, we apply the particle swarm optimization-backpropagation model and an attention mechanism to calculate the initial weights of evaluation attributes for reflecting the influence of different attributes on doctor rankings. After that, based on doctors’ behaviors and performances, and their impact on platform ecology and patient experience, we obtain the variable weights by modifying the initial weights of contribution attributes through an incentive and penalty mechanism and a state variable weight function, thereby reflecting the impact of doctors’ contributions on attribute weights. Additionally, we employ a score function with variable weights to calculate doctors’ scores and rankings, highlighting the effect of doctors’ contributions on their rankings. The proposed doctor ranking method is validated with a real dataset of 131,721 messages about 11,539 cardiovascular doctors from the well-known Chinese online medical consultation platform, Haodf.com. Ranking results of ten randomly selected doctors show that six doctors experience ranking changes due to their higher or lower levels of contribution, confirming the method’s effectiveness. Sensitivity analysis and evaluations of accuracy and usability further validate the robustness and reliability. Comparative experiments with state-of-the-art large language models and baseline methods reveal that the proposed method captures the complex interactions and latent relationships within large-scale online medical data, and more directly and clearly reflects the influence of doctors’ different levels of contribution on their rankings, providing a distinct advantage in incentivizing or penalizing doctors with exceptional or subpar performance.

当前在线医疗咨询平台面临医生参与度低的挑战，严重影响了服务质量和可持续发展。现有的医生排名机制未能有效调动医生的积极参与，充分发挥医生的贡献潜力。因此，本文从在线医疗咨询平台的角度出发，提出了一种新颖的医生排名方法，以激发医生的参与度。具体而言，本文首先构建了一个基于不完全在线数据的综合医生评价属性体系。然后，我们应用粒子群优化-反向传播模型和注意机制计算评价属性的初始权重，以反映不同属性对医生排名的影响。然后，根据医生的行为和表现，以及对平台生态和患者体验的影响，通过激励惩罚机制和状态变量权重函数对贡献属性的初始权重进行修改，得到变量权重，从而反映医生的贡献对属性权重的影响。此外，我们使用了一个可变权重的分数函数来计算医生的分数和排名，突出了医生的贡献对他们排名的影响。通过中国知名在线医疗咨询平台好医生网的11,539名心血管医生的131,721条消息的真实数据集验证了所提出的医生排名方法。随机选取的10位医生的排名结果显示，有6位医生因贡献水平的高低而出现排名变化，证实了该方法的有效性。灵敏度分析和准确性和可用性评价进一步验证了鲁棒性和可靠性。与最先进的大型语言模型和基线方法的比较实验表明，该方法捕获了大规模在线医疗数据中复杂的相互作用和潜在关系，更直接、更清晰地反映了医生不同贡献水平对其排名的影响，在激励或惩罚表现优异或差等的医生方面提供了明显的优势。

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引用次数: 0