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Spatio-temporal attention based collaborative local–global learning for traffic flow prediction 基于时空注意力的局部-全局协作学习,用于交通流量预测
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-11 DOI: 10.1016/j.engappai.2024.109575
Haiyang Chi , Yuhuan Lu , Can Xie , Wei Ke , Bidong Chen
Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.
交通流预测对智能交通系统(ITS)至关重要,可为交通控制、路线规划和运营管理提供有价值的见解。现有研究通常将空间依赖性和时间依赖性分开建模,并主要依赖预定义的图形来表示时空依赖性,从而忽略了突发事件引起的交通动态以及路段之间的全局关系。与以往主要关注局部特征提取的模型不同,我们提出了一种新颖的局部-全局协作学习模型(LOGO),该模型采用了时空注意力(STA)和图卷积网络(GCN)。具体来说,LOGO 可同时从局部和全局角度提取隐藏的交通特征。在局部特征提取方面,设计了一种新颖的时空注意力(STA),以直接关注时空耦合的相互依赖关系,而不是分别对时间和空间依赖关系进行建模,并通过关注交通流动态的自适应图来捕捉深入的时空交通背景。在全局特征提取方面,构建了全局相关矩阵,并利用 GCN 在矩阵上传播信息,以实现相邻和相似路段之间的互动。最后,将获得的局部和全局特征串联起来,并输入门控聚合,以预测未来的交通流量。在加州交通局性能测量系统(PEMS03、PEMS04、PEMS07 和 PEMS08)提供的四个真实交通数据集上进行的大量实验证明了我们所建议的模型的有效性。在 PEMS07 数据集上,LOGO 的性能超过了 18 个最先进的基线,并取得了最佳预测性能,最高提高了 6.06%。此外,两项实际案例研究进一步证实了LOGO的稳健性和可解释性。
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引用次数: 0
Elite-based multi-objective improved iterative local search algorithm for time-dependent vehicle-drone collaborative routing problem with simultaneous pickup and delivery 基于精英的多目标改进迭代局部搜索算法,用于同时取货和送货的随时间变化的车辆-无人机协作路由问题
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-11 DOI: 10.1016/j.engappai.2024.109608
Haohao Duan , Xiaoling Li , Guanghui Zhang , Yanxiang Feng , Qingchang Lu
This paper focuses on solving a time-dependent multi-objective vehicle-drone collaborative routing problem with simultaneous pickup and delivery, in which multiple visits per drone trip, simultaneous pickup and delivery, soft time windows, and time-dependent road network are considered. With the maximum completion time and total violation time as the optimization objectives, we first formulate the mathematical model of the problem. Then, in order to effectively solve the problem, an Elite-based multi-objective improved iterative local search algorithm developed within a collaborative optimization framework is proposed. Specifically, the multi-objective problem is decomposed into two subproblems, each of which is solved by minimizing a single objective. Meanwhile, the algorithm uses an elite set to record non-dominated solutions, guide the search, and achieve information exchange between subproblems. In the proposed algorithm, an individual is encoded as a vector consisting of two parts, a customer sequence and a sequence recording the customers' visiting modes, and can be decoded into subroutes for the vehicle and drone. To guarantee the feasibility of the solution, an adjustment method is proposed to repair the individual. In addition, based on individual representation and problem characteristics, six neighborhood structures are designed, through which new individuals can be generated. Then, by using the neighborhood structures, a problem-specific local search strategy and an iterative local search strategy are proposed to improve the search capability of the algorithm. Experimental tests and analyses demonstrate the correctness of the established mathematical model and the effectiveness of the proposed algorithm in solving this complex vehicle-drone collaborative routing problem.
本文重点解决了一个与时间相关的同时取货和送货的多目标车辆-无人机协同路由问题,其中考虑了无人机的一次行程多次访问、同时取货和送货、软时间窗口以及与时间相关的道路网络。以最长完成时间和总违规时间为优化目标,我们首先建立了问题的数学模型。然后,为了有效地解决该问题,我们提出了一种在协同优化框架内开发的基于 Elite 的多目标改进迭代局部搜索算法。具体来说,多目标问题被分解成两个子问题,每个子问题通过最小化单一目标来解决。同时,该算法使用精英集记录非主导解,引导搜索,并实现子问题之间的信息交流。在所提出的算法中,个体被编码为由客户序列和记录客户访问模式的序列两部分组成的向量,并可解码为车辆和无人机的子路线。为了保证解决方案的可行性,提出了一种调整方法来修复个体。此外,根据个体表示和问题特征,设计了六个邻域结构,通过这些邻域结构可以生成新的个体。然后,利用邻域结构,提出了针对具体问题的局部搜索策略和迭代局部搜索策略,以提高算法的搜索能力。实验测试和分析证明了所建立的数学模型的正确性,以及所提出的算法在解决这一复杂的车辆-无人机协作路由问题中的有效性。
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引用次数: 0
Self-learning guided residual shrinkage network for intelligent fault diagnosis of planetary gearbox 用于行星齿轮箱智能故障诊断的自学习引导残差收缩网络
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-10 DOI: 10.1016/j.engappai.2024.109603
Xingwang Lv , Jinrui Wang , Ranran Qin , Jihua Bao , Xue Jiang , Zongzhen Zhang , Baokun Han , Xingxing Jiang
The original vibration signals of the fault gear under different working conditions have a large distribution difference, and there will be insufficient feature extraction during fault diagnosis, which leads to the problem of low diagnostic accuracy. Therefore, a self-learning model based on residual shrinkage network (SLRSN) is proposed. The model constructs a deep residual shrinkage network as the main network for feature extraction of the original vibration signal to enhance the robustness of the model. Then self-believing loss and self-doubting loss are proposed to achieve self-confidence and suspicion of health status prediction. The first is self-confidence loss, which adopts sub-domain distribution adaptation to actively align learned cross-domain features. The second is self-doubt loss, which provides SLRSN with the ability to extricate from wrong experience. Finally, to mitigate the effects of negative transfer, a novel adaptative weight allocation mechanism is designed to recalibrate the weighting of each source domain sample. Through the experiment of two gearboxes, it is verified that the proposed SLRSN method has good diagnostic reliability under the condition of gear speed and load change.
不同工况下故障齿轮的原始振动信号分布差异较大,在故障诊断过程中会出现特征提取不充分,导致诊断准确率低的问题。因此,本文提出了一种基于残差收缩网络(SLRSN)的自学习模型。该模型构建了一个深度残差收缩网络,作为对原始振动信号进行特征提取的主要网络,以增强模型的鲁棒性。然后提出了自信损失和自疑损失,以实现对健康状况的自信和怀疑预测。第一种是自信损失,它采用子域分布自适应来主动调整学习到的跨域特征。其次是自我怀疑损失,它为 SLRSN 提供了从错误经验中解脱出来的能力。最后,为减轻负迁移的影响,设计了一种新颖的自适应权重分配机制,以重新校准每个源域样本的权重。通过两个齿轮箱的实验,验证了所提出的 SLRSN 方法在齿轮转速和负载变化条件下具有良好的诊断可靠性。
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引用次数: 0
Etching process prediction based on cascade recurrent neural network 基于级联递归神经网络的蚀刻工艺预测
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-08 DOI: 10.1016/j.engappai.2024.109590
Zhenjie Yao , Ziyi Hu , Panpan Lai , Fengling Qin , Wenrui Wang , Zhicheng Wu , Lingfei Wang , Hua Shao , Yongfu Li , Zhiqiang Li , Zhongming Liu , Junjie Li , Rui Chen , Ling Li
Etching is one of the most critical processes in semiconductor manufacturing. Etch models have been developed to reveal the underlying etch mechanisms, which employs rigorous physical and chemical process simulation. Traditional simulation is very time consuming. The data-driven artificial intelligence model provides an alternative modeling approach. In this paper, a Cascade Recurrent Neural Networks (CRNN) is proposed to model and predict etching profiles. The etching profile is represented by polar coordinates and modeled by the recurrent neural networks, the corresponding etching parameters (e.g., pressure, power, temperature, and voltage) are integrated into the network through cascade combination layers. Experimental results on a dataset of 10,000 simulated etching profiles demonstrated the effectiveness of our method: compared with traditional etching simulation methods, CRNN can speedup 21,000× with an average error of less than 0.7 nm for 1 step prediction. Furthermore, compared to simple deep neural networks, the Mean Absolute Errors (MAE) could be reduced from 1.7329 nm to 1.3845 nm for 10 steps prediction. Finally, the effectiveness and accuracy of CRNN etching predictor is validated through fine-tuning on experimental data.
蚀刻是半导体制造中最关键的工艺之一。为了揭示蚀刻的基本机制,人们开发了蚀刻模型,采用了严格的物理和化学过程模拟。传统的模拟非常耗时。数据驱动的人工智能模型提供了另一种建模方法。本文提出了一种级联递归神经网络(CRNN)来模拟和预测蚀刻曲线。蚀刻曲线由极坐标表示,并由递归神经网络建模,相应的蚀刻参数(如压力、功率、温度和电压)通过级联组合层集成到网络中。在 10,000 个模拟蚀刻曲线数据集上的实验结果证明了我们方法的有效性:与传统的蚀刻模拟方法相比,CRNN 的速度提高了 21,000 倍,1 步预测的平均误差小于 0.7 nm。此外,与简单的深度神经网络相比,10 步预测的平均绝对误差(MAE)可从 1.7329 nm 降至 1.3845 nm。最后,通过对实验数据进行微调,验证了 CRNN 蚀刻预测器的有效性和准确性。
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引用次数: 0
Attention-based hand pose estimation with voting and dual modalities 基于注意力的手部姿态估计,采用投票和双模技术
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-08 DOI: 10.1016/j.engappai.2024.109526
Dinh-Cuong Hoang , Anh-Nhat Nguyen , Thu-Uyen Nguyen , Ngoc-Anh Hoang , Van-Duc Vu , Duy-Quang Vu , Phuc-Quan Ngo , Khanh-Toan Phan , Duc-Thanh Tran , Van-Thiep Nguyen , Quang-Tri Duong , Ngoc-Trung Ho , Cong-Trinh Tran , Van-Hiep Duong , Anh-Truong Mai
Hand pose estimation has recently emerged as a compelling topic in the robotic research community, because of its usefulness in learning from human demonstration or safe human–robot interaction. Although deep learning-based methods have been introduced for this task and have shown promise, it remains a challenging problem. To address this, we propose a novel end-to-end architecture for hand pose estimation using red-green-blue (RGB) and depth (D) data (RGB-D). Our approach processes the two data sources separately and utilizes a dense fusion network with an attention module to extract discriminative features. The features extracted include both spatial information and geometric constraints, which are fused to vote for the hand pose. We demonstrate that our voting mechanism in conjunction with the attention mechanism is particularly useful for solving the problem, especially when hands are heavily occluded by objects or are self-occluded. Our experimental results on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods by a significant margin.
手部姿态估计最近成为机器人研究领域一个引人注目的课题,因为它有助于从人类演示或安全的人机交互中学习。虽然基于深度学习的方法已被引入到这项任务中,并显示出良好的前景,但它仍然是一个具有挑战性的问题。为了解决这个问题,我们提出了一种新颖的端到端架构,利用红-绿-蓝(RGB)和深度(D)数据(RGB-D)进行手部姿态估计。我们的方法分别处理两个数据源,并利用密集融合网络和注意力模块来提取辨别特征。提取的特征包括空间信息和几何约束,通过融合这些特征来对手部姿势进行投票。我们证明,我们的投票机制与注意力机制相结合,对解决这一问题特别有用,尤其是当手被物体严重遮挡或自我遮挡时。我们在基准数据集上的实验结果表明,我们的方法明显优于最先进的方法。
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引用次数: 0
Region-guided spatial feature aggregation network for vehicle re-identification 用于车辆再识别的区域导向空间特征聚合网络
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-08 DOI: 10.1016/j.engappai.2024.109568
Yanzhen Xiong , Jinjia Peng , Zeze Tao , Huibing Wang
In the context of the advancement of smart city management, re-identification technology has emerged as an area of particular interest and research in the field of artificial intelligence, especially vehicle re-identification (re-ID), which aims to identify target vehicles in multiple non-overlapping fields of view. Most existing methods rely on fine-grained cues in the salient regions. Although impressive results have been achieved, these methods typically require additional auxiliary networks to localize the salient regions containing fine-grained cues. Meanwhile, changes in state such as illumination, viewpoint and occlusion can affect the position of the salient regions. To solve the above problems, this paper proposes a Region-guided Spatial Feature Aggregation Network (RSFAN) for vehicle re-ID, which forces the model to learn the latent information in the minor salient regions. Firstly, a Regional Localization (RL) module is proposed to automatically locate the salient regions without additional auxiliary networks. In addition, to mitigate the misguidance caused by the inaccurate salient regions, a Spatial Feature Aggregation (SFA) module is designed to weaken and enhance the expression of the salient and minor salient regions, respectively. Meanwhile, to enhance the diversity of the minor salient region-related information, a Cross-level Channel Attention (CCA) module is designed to implement cross-level interactions through the channel attention mechanism across different levels. Finally, to constrain the distributional differences between the salient regions and minor salient regions feature, a Distributional Variance (DV) loss is proposed. The extensive experiments show that the RSFAN has a good performance on VeRi-776, VehicleID, VeRi-Wild and Market1501 datasets.
在推进智能城市管理的背景下,重新识别技术已成为人工智能领域特别关注和研究的一个领域,尤其是车辆重新识别(re-ID),其目的是在多个非重叠视场中识别目标车辆。现有的大多数方法都依赖于突出区域的细粒度线索。虽然已经取得了令人瞩目的成果,但这些方法通常需要额外的辅助网络来定位包含细粒度线索的突出区域。同时,光照、视角和遮挡等状态的变化也会影响突出区域的位置。为了解决上述问题,本文提出了一种用于车辆再识别的区域引导空间特征聚合网络(RSFAN),它迫使模型学习次要突出区域中的潜在信息。首先,本文提出了一个区域定位(RL)模块,无需额外的辅助网络即可自动定位突出区域。此外,为了减少因突出区域不准确而造成的误导,还设计了一个空间特征聚合(SFA)模块,以分别弱化和增强突出区域和次突出区域的表达。同时,为了增强次要突出区域相关信息的多样性,设计了一个跨级通道注意(CCA)模块,通过跨级通道注意机制实现跨级交互。最后,为了限制突出区域和次要突出区域特征之间的分布差异,提出了分布方差(DV)损失。大量实验表明,RSFAN 在 VeRi-776、VehicleID、VeRi-Wild 和 Market1501 数据集上具有良好的性能。
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引用次数: 0
A fault diagnosis framework using unlabeled data based on automatic clustering with meta-learning 基于元学习自动聚类的非标记数据故障诊断框架
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-08 DOI: 10.1016/j.engappai.2024.109584
Zhiqian Zhao , Yinghou Jiao , Yeyin Xu , Zhaobo Chen , Enrico Zio
With the growth of the industrial internet of things, the poor performance of conventional deep learning models hinders the application of intelligent diagnosis methods in industrial situations such as lack of fault samples and difficulties in data labeling. To solve the above problems, we propose a fault diagnosis framework based on unsupervised meta-learning and contrastive learning, which is called automatic clustering with meta-learning (ACML). First, the amount of data is expanded through data augmentation approaches, and a feature generator is constructed to extract highly discriminative features from the unlabeled dataset using contrastive learning. Then, a cluster generator is used to automatically divide cluster partitions and add pseudo-labels for these. Finally, the classification tasks are derived through taking original samples in the partitions, which are embedded in the meta-learner for fault diagnosis. In the meta-learning stage, we split out two subsets from task and feed them into the inner and outer loops to maintain the class consistency of the real labels. After training, ACML transfers its prior expertise to the unseen task to efficiently complete the categorization of new faults. ACML is applied to two cases concerning a public dataset and a self-constructed dataset, demonstrate that ACML achieves good diagnostic performance, outperforming popular unsupervised methods.
随着工业物联网的发展,传统深度学习模型的性能不佳阻碍了智能诊断方法在工业领域的应用,如缺乏故障样本和数据标注困难等。为解决上述问题,我们提出了一种基于无监督元学习和对比学习的故障诊断框架,即元学习自动聚类(ACML)。首先,通过数据扩增方法扩大数据量,并构建特征生成器,利用对比学习从未标明的数据集中提取高分辨特征。然后,使用聚类生成器自动划分聚类分区,并为这些分区添加伪标签。最后,通过提取分区中的原始样本,得出分类任务,并将其嵌入元学习器,用于故障诊断。在元学习阶段,我们从任务中分离出两个子集,并将其分别输入内循环和外循环,以保持真实标签的类一致性。训练完成后,ACML 将其先前的专业知识转移到未见任务中,从而高效地完成新故障的分类。我们将 ACML 应用于公共数据集和自建数据集两个案例,结果表明 ACML 实现了良好的诊断性能,优于流行的无监督方法。
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引用次数: 0
Code-switching finetuning: Bridging multilingual pretrained language models for enhanced cross-lingual performance 代码转换微调:连接多语言预训练语言模型,提高跨语言性能
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-07 DOI: 10.1016/j.engappai.2024.109532
Changtong Zan , Liang Ding , Li Shen , Yu Cao , Weifeng Liu
In recent years, the development of pre-trained models has significantly propelled advancements in natural language processing. However, multilingual sequence-to-sequence pretrained language models (Seq2Seq PLMs) are pretrained on a wide range of languages (e.g., 25 languages), yet often finetuned for specific bilingual tasks (e.g., English–German), leading to domain and task discrepancies between pretraining and finetuning stages, which may lead to sub-optimal downstream performance. In this study, we first illustratively reveal such domain and task discrepancies, and then conduct an in-depth investigation into the side effects that these discrepancies may have on both training dynamic and downstream performance. To alleviate those side effects, we introduce a simple and effective code-switching restoration task (namely code-switching finetuning) into the standard pretrain-finetune pipeline. Specifically, in the first stage, we recast the downstream data as the self-supervised format used for pretraining, in which the denoising signal is the code-switched cross-lingual phrase. Then, the model is finetuned on downstream task as usual in the second stage. Experiments spanning both natural language generation (12 supervised translations, 30 zero-shot translations, and 2 cross-lingual summarization tasks) and understanding (7 cross-lingual natural language inference tasks) tasks demonstrate that our model consistently and significantly surpasses the standard finetuning strategy. Analyses show that our method introduces negligible computational cost and reduces cross-lingual representation gaps. We have made the code publicly available at: https://github.com/zanchangtong/CSR4mBART.
近年来,预训练模型的发展极大地推动了自然语言处理技术的进步。然而,多语言序列到序列预训练语言模型(Seq2Seq PLMs)是在广泛的语言(如 25 种语言)上进行预训练的,但往往针对特定的双语任务(如英语-德语)进行微调,导致预训练和微调阶段之间的领域和任务差异,这可能会导致下游性能达不到最优。在本研究中,我们首先揭示了这种领域和任务差异,然后深入研究了这些差异可能对训练动态和下游性能产生的副作用。为了减轻这些副作用,我们在标准的预训练-微调流水线中引入了一个简单有效的代码转换恢复任务(即代码转换微调)。具体来说,在第一阶段,我们将下游数据重铸为用于预训练的自监督格式,其中去噪信号为代码转换后的跨语言短语。然后,在第二阶段,像往常一样在下游任务中对模型进行微调。横跨自然语言生成(12 个监督翻译、30 个零镜头翻译和 2 个跨语言总结任务)和理解(7 个跨语言自然语言推理任务)任务的实验表明,我们的模型持续且显著地超越了标准的微调策略。分析表明,我们的方法带来的计算成本可以忽略不计,而且减少了跨语言表征差距。我们已在以下网址公开了代码:https://github.com/zanchangtong/CSR4mBART。
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引用次数: 0
Adaptive masked network for ultra-short-term photovoltaic forecast 用于超短期光伏预测的自适应遮蔽网络
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-07 DOI: 10.1016/j.engappai.2024.109555
Qiaoyu Ma , Xueqian Fu , Qiang Yang , Dawei Qiu
In recent years, power grid companies have faced increasingly stringent requirements for accurate prediction of photovoltaic (PV) power generation with the rapid development of PV technologies. In ultra-short-term forecasting, PV power generation exhibits strong temporal correlations, leading to high data redundancy. To address this issue, we propose an adaptive masked network (ASMNet) to enhance the accuracy of ultra-short-term PV forecasting. Specifically, this method improves the feature extraction of short-term fluctuations within historical time periods by down-weighting less significant temporal segments during the learning process. It captures the uncertain effects of environmental changes and provides a better understanding of the impacts of ultra-short-term fluctuations. We test our model on three public PV power generation datasets, and it achieves the best performance with a root mean square error of 21.42, 0.2824 and 23.36 for the Belgian, American National Renewable Energy Laboratory, and Desert Knowledge Australia Solar Center datasets, respectively. Additionally, the proposed model demonstrates a 0.01%–0.50% improvement in coefficient of determination compared to baseline models across all datasets, highlighting its superior performance and effectiveness in ultra-short-term PV forecasting.
近年来,随着光伏技术的快速发展,电网公司对光伏发电量精确预测的要求越来越严格。在超短期预测中,光伏发电量表现出很强的时间相关性,导致数据冗余度很高。针对这一问题,我们提出了一种自适应掩蔽网络(ASMNet),以提高光伏发电超短期预测的准确性。具体来说,该方法通过在学习过程中降低不重要时间片段的权重,改进了历史时间段内短期波动的特征提取。它捕捉到了环境变化的不确定影响,并能更好地理解超短期波动的影响。我们在三个公共光伏发电数据集上测试了我们的模型,结果表明该模型性能最佳,比利时、美国国家可再生能源实验室和澳大利亚沙漠知识太阳能中心数据集的均方根误差分别为 21.42、0.2824 和 23.36。此外,在所有数据集上,与基线模型相比,拟议模型的判定系数提高了 0.01%-0.50%,突显了其在超短期光伏预测方面的卓越性能和有效性。
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引用次数: 0
Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic 基于仿真的遗传算法优化大流行病中的城市合作废物供应链
IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-11-07 DOI: 10.1016/j.engappai.2024.109478
Peiman Ghasemi , Alireza Goli , Fariba Goodarzian , Jan Fabian Ehmke
The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.
城市产生的医疗废物数量不断增加,可能对环境和人类健康造成重大危害。开发一个强大的供应链网络来管理城市医疗废物对社会非常重要,尤其是在 COVID-19 这样的大流行病期间。在供应链网络设计中,非感染性废物的收集、将感染性废物从医院运送到处置设施、废物变能源项目的创收以及大流行病爆发的可能性等因素往往被忽视。因此,在本研究中,我们设计了一个包含 COVID-19 参数的模型,以便在大流行期间设计有效的城市医疗废物供应链网络的同时,减少病毒的传播。所提出的模型具有多目标、多区域、多商品的特点,并涉及基于联盟的合作。第一个目标函数旨在最大限度地降低总成本,第二个目标则是最大限度地降低 COVID-19 爆发的风险。我们确定了城市医疗废物收集中心之间的最佳合作方式,以最大限度地节约成本。根据每个区域的居民情况,计算出该区域医疗废物的 COVID-19 流行风险水平。此外,我们还分析了一个系统动态模拟框架,以预测 COVID-19 条件下的废物产生水平。我们使用基于非支配排序遗传算法 II 的元启发式来解决该问题,并以精确解作为基准。为了说明我们的方法,我们以伊朗德黑兰为重点进行了案例研究。结果表明,废物产生量的增加会导致供应链总成本的增加。
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引用次数: 0
期刊
Engineering Applications of Artificial Intelligence
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