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WGO: a similarly encoded whale-goshawk optimization algorithm for uncertain cloud manufacturing service composition
Pub Date : 2025-03-05 DOI: 10.1007/s43684-025-00089-x
Kezhou Chen, Tao Wang, Huimin Zhuo, Lianglun Cheng

Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently, digital twins have gained prominence in CMfg due to their predictive capabilities, enhancing the reliability of service composition. Heuristic algorithms are widely used in this field for their flexibility and compatibility with uncertain environments. This paper proposes the Whale-Goshawk Optimization Algorithm (WGO), which combines the Whale Optimization Algorithm (WOA) and Northern Goshawk Optimization Algorithm (NGO). A novel similar integer coding method, incorporating spatial feature information, addresses the limitations of traditional integer coding, while a whale-optimized prey generation strategy improves NGO’s global optimization efficiency. Additionally, a local search method based on similar integer coding enhances WGO’s local search ability. Experimental results demonstrate the effectiveness of the proposed approach.

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引用次数: 0
Explanation framework for industrial recommendation systems based on the generative adversarial network with embedding constraints
Pub Date : 2025-03-03 DOI: 10.1007/s43684-025-00092-2
Binchuan Qi, Wei Gong, Li Li

The explainability of recommendation systems refers to the ability to explain the logic that guides the system’s decision to endorse or exclude an item. In industrial-grade recommendation systems, the high complexity of features, the presence of embedding layers, the existence of adversarial samples and the requirements for explanation accuracy and efficiency pose significant challenges to current explanation methods. This paper proposes a novel framework AdvLIME (Adversarial Local Interpretable Model-agnostic Explanation) that leverages Generative Adversarial Networks (GANs) with Embedding Constraints to enhance explainability. This method utilizes adversarial samples as references to explain recommendation decisions, generating these samples in accordance with realistic distributions and ensuring they meet the structural constraints of the embedding module. AdvLIME requires no modifications to the existing model architecture and needs only a single training session for global explanation, making it ideal for industrial applications. This work contributes two significant advancements. First, it develops a model-independent global explanation method via adversarial generation. Second, it introduces a model discrimination method to guarantee that the generated samples adhere to the embedding constraints. We evaluate the AdvLIME framework on the Behavior Sequence Transformer (BST) model using the MovieLens 20 M dataset. The experimental results show that AdvLIME outperforms traditional methods such as LIME and DLIME, reducing the approximation error of real samples by 50% and demonstrating improved stability and accuracy.

推荐系统的可解释性是指解释系统决定认可或排除某个项目的逻辑的能力。在工业级推荐系统中,特征的高复杂性、嵌入层的存在、对抗样本的存在以及对解释准确性和效率的要求,都对当前的解释方法提出了巨大挑战。本文提出了一个新颖的框架 AdvLIME(对抗性本地可解释模型-不可知解释),利用具有嵌入约束的生成对抗网络(GAN)来增强可解释性。这种方法利用对抗样本作为参考来解释推荐决策,按照现实分布生成这些样本,并确保它们符合嵌入模块的结构约束。AdvLIME 无需修改现有的模型架构,只需进行一次全局解释训练,因此非常适合工业应用。这项工作有两个重大进展。首先,它通过对抗生成开发了一种与模型无关的全局解释方法。其次,它引入了一种模型判别方法,以保证生成的样本符合嵌入约束条件。我们使用 MovieLens 20 M 数据集对行为序列转换器(BST)模型上的 AdvLIME 框架进行了评估。实验结果表明,AdvLIME 优于 LIME 和 DLIME 等传统方法,真实样本的近似误差减少了 50%,稳定性和准确性也得到了提高。
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引用次数: 0
Adaptive control of bilateral teleoperation systems under denial-of-service attacks
Pub Date : 2025-02-26 DOI: 10.1007/s43684-025-00093-1
Lanyan Wei, Yuling Li

This paper investigates resilient consensus control for teleoperation systems under denial-of-service (DoS) attacks. We design resilient controllers with auxiliary systems based on sampled positions of both master and slave robots, enhancing robustness during DoS attacks. Additionally, we establish stability conditions on DoS attack duration and frequency by applying multivariate small-gain methods to ensure closed-loop stability without the need to solve linear matrix inequalities. Finally, the effectiveness of the controllers is validated through the simulation results, demonstrating that the master-slave synchronization is achieved.

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引用次数: 0
Efficient and accurate road crack detection technology based on YOLOv8-ES
Pub Date : 2025-02-10 DOI: 10.1007/s43684-025-00091-3
Kaili Zeng, Rui Fan, Xiaoyu Tang

Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.

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引用次数: 0
A cooperative jamming decision-making method based on multi-agent reinforcement learning
Pub Date : 2025-02-05 DOI: 10.1007/s43684-025-00090-4
Bingchen Cai, Haoran Li, Naimin Zhang, Mingyu Cao, Han Yu

Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.

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引用次数: 0
Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP 基于动态时间关注和混合MLP的增强轴承RUL预测
Pub Date : 2025-01-10 DOI: 10.1007/s43684-024-00088-4
Zhongtian Jin, Chong Chen, Aris Syntetos, Ying Liu

Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

轴承是机械中的关键部件,准确预测其剩余使用寿命(RUL)对于有效的预测性维护至关重要。传统的RUL预测方法通常依赖于手动特征提取和专家知识,这面临着特殊的挑战,例如处理非平稳数据以及避免由于包含大量不相关特征而导致的过拟合。本文提出了一种利用连续小波变换(CWT)进行特征提取的方法,一个通道-时间混合MLP (CT-MLP)层用于捕获复杂的依赖关系,以及一个基于时间序列中特征的时间重要性调整其焦点的动态注意机制。动态注意机制将多头注意与创新增强相结合,使其对表现出非平稳行为的数据集特别有效。使用XJTU-SY滚动轴承数据集和PRONOSTIA轴承数据集进行的实验研究表明,所提出的深度学习算法在RMSE和MAE方面显著优于其他最先进的算法,证明了其鲁棒性和准确性。
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引用次数: 0
An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles 一种适用于联网和自动驾驶汽车的互动公平的半分散轨迹规划器
Pub Date : 2025-01-03 DOI: 10.1007/s43684-024-00087-5
Zhengqin Liu, Jinlong Lei, Peng Yi, Yiguang Hong

Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constraints in a CAV trajectory planning problem.

近年来,人们对自动驾驶汽车轨迹规划的博弈论方法产生了浓厚的兴趣。但大多数方法对每个自动驾驶汽车独立求解博弈,缺乏协调机制,导致计算冗余,无法收敛到同一均衡,对计算效率和安全性提出了挑战。此外,大多数研究都依赖于了解所有其他自动驾驶汽车意图的强烈假设。本文利用车对万物(V2X)技术,设计了一种新的自动驾驶车辆轨迹规划方法,以解决非协调轨迹规划中的计算效率和安全问题。首先,将网联自动驾驶汽车的轨迹规划表述为具有耦合安全约束的博弈。然后,我们定义了计划轨迹的交互公平性,并证明了交互公平性轨迹对应于该博弈的变分均衡。随后,我们提出了一种半分散的车辆规划方案,以寻求基于V2X的公平轨迹,其中每个CAV根据通过V2X共享的相邻CAV的信息优化其个人轨迹,路边单元承担更新避碰约束乘数的作用。该方法通过对机动车辆之间的并行计算,显著提高了计算效率,并通过保证机动车辆之间的平衡一致性,提高了规划轨迹的安全性。最后,我们在十字路口进行了多种情况下的蒙特卡罗实验,实验结果表明,SVEP具有计算速度快、通信载荷小、可扩展性强、平衡一致性好、安全性高等优点,是一种很有前景的互联交通场景下的轨迹规划解决方案。据我们所知,这是第一个在CAV轨迹规划问题中实现具有耦合约束的半分布式求解的研究。
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引用次数: 0
Network synchronizability enhancement via adding antagonistic interactions 通过添加拮抗相互作用增强网络同步性
Pub Date : 2024-12-31 DOI: 10.1007/s43684-024-00086-6
Yue Song, Xiaoqin Liu, Dingmei Wang, Pengfei Gao, Mengqi Xue

We discover a “less-is-more” effect that adding local antagonistic interactions (negative edge weights) can enhance the overall synchronizability of a dynamical network system. To explain this seemingly counterintuitive phenomenon, a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying network. We further reveal that this condition can be interpreted from the perspective of resistance distance and network community structure. The obtained result is also verified via numerical experiments on a 14-node network and a 118-node network. Our finding brings new thoughts and inspirations to the future directions of optimal network design problems.

我们发现了一个“少即是多”的效应,即增加局部对抗相互作用(负边权)可以增强动态网络系统的整体同步性。为了解释这种看似违反直觉的现象,我们建立了一个条件来识别那些权重降低可以提高底层网络同步性指数的边缘。我们进一步发现,这种情况可以从抵抗距离和网络社区结构的角度来解释。并通过14节点网络和118节点网络的数值实验对所得结果进行了验证。这一发现为未来网络优化设计问题的研究方向提供了新的思路和启示。
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引用次数: 0
Distributed strategies for pursuit-evasion of high-order integrators 高阶集成商追逃的分布式策略
Pub Date : 2024-12-27 DOI: 10.1007/s43684-024-00085-7
Panpan Zhou, Yueyue Xu, Bo Wahlberg, Xiaoming Hu

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

本文提出了具有联盟内合作和联盟间对抗的高阶积分器追逃问题的分散解。引入了不同的误差变量和超变量,以保证控制策略不受相对速度、加速度和邻居的高阶信息的影响。因此,我们的方法只需要代理交换位置信息或测量邻居的相对位置。分布式策略考虑了参与者的联盟内合作或联盟间对抗的目标。在建立了一类高阶积分器的充要条件后,给出了一类高阶积分器在单跟踪器- 1逃避器、多跟踪器- 1逃避器和多跟踪器-多逃避器三种情况下的捕获和编队控制的指数收敛条件。结果表明,这些条件取决于通信图的结构、控制律中的权值和期望的编队构型。最后,通过仿真结果验证了该算法的有效性。
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引用次数: 0
An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion 一种基于自动特征提取和自适应数据融合的表面粗糙度智能预测方法
Pub Date : 2024-12-12 DOI: 10.1007/s43684-024-00083-9
Xun Zhang, Sibao Wang, Fangrui Gao, Hao Wang, Haoyu Wu, Ying Liu

Machining quality prediction based on cutting big data is the core focus of current developments in intelligent manufacturing. Presently, predictions of machining quality primarily rely on process and signal analyses. Process-based predictions are generally constrained to the development of rudimentary regression models. Signal-based predictions often require large amounts of data, multiple processing steps (such as noise reduction, principal component analysis, modulation, etc.), and have low prediction efficiency. In addition, the accuracy of the model depends on tedious manual parameter tuning. This paper proposes a convolutional neural network quality intelligent prediction model based on automatic feature extraction and adaptive data fusion (CNN-AFEADF). Firstly, by processing signals from multiple directions, time-frequency domain images with rich features can be obtained, which significantly benefit neural network learning. Secondly, the corresponding images in three directions are fused into one image by setting different fusion weight parameters. The optimal fusion weight parameters and window length are determined by the Particle Swarm Optimization algorithm (PSO). This data fusion method reduces training time by 16.74 times. Finally, the proposed method is verified by various experiments. This method can automatically identify sensitive data features through neural network fitting experiments and optimization, thereby eliminating the need for expert experience in determining the significance of data features. Based on this approach, the model achieves an average relative error of 2.95%, reducing the prediction error compared to traditional models. Furthermore, this method enhances the intelligent machining level.

基于切削大数据的加工质量预测是当前智能制造发展的核心方向。目前,加工质量的预测主要依赖于过程和信号分析。基于过程的预测通常局限于基本回归模型的发展。基于信号的预测往往需要大量的数据,多个处理步骤(如降噪、主成分分析、调制等),并且预测效率较低。此外,模型的准确性依赖于繁琐的手动参数调整。提出了一种基于自动特征提取和自适应数据融合的卷积神经网络质量智能预测模型(CNN-AFEADF)。首先,通过对来自多个方向的信号进行处理,可以获得特征丰富的时频域图像,这对神经网络的学习有很大的帮助。其次,通过设置不同的融合权值参数,将三个方向对应的图像融合为一幅图像;采用粒子群优化算法(PSO)确定最优融合权参数和窗口长度。这种数据融合方法将训练时间缩短了16.74倍。最后,通过各种实验验证了所提出的方法。该方法可以通过神经网络拟合实验和优化自动识别敏感数据特征,从而消除了确定数据特征重要性时需要专家经验的需要。基于该方法,模型的平均相对误差为2.95%,与传统模型相比,降低了预测误差。进一步提高了智能化加工水平。
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引用次数: 0
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