Complex & Intelligent Systems最新文献_第5页

Resource state adaptive collaboration mechanism based on resource modeling and multi-agent system 基于资源建模和多代理系统的资源状态自适应协作机制

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01882-0

Zhengzuo Li, Chengxi Piao, Dianhui Chu, Zhiying Tu, Xin Hu, Deqiong Ding

{"title":"Resource state adaptive collaboration mechanism based on resource modeling and multi-agent system","authors":"Zhengzuo Li, Chengxi Piao, Dianhui Chu, Zhiying Tu, Xin Hu, Deqiong Ding","doi":"10.1007/s40747-025-01882-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01882-0","url":null,"abstract":"The management of complex, dynamic, and cross-domain resources in cyber-physical-human systems (CPHS) faces significant challenges under spatiotemporal dynamics, particularly resource state conflicts caused by rapid environmental changes and interdependent resource interactions. To address these challenges, this study proposes an integrated framework combining resource modeling and resource state adaptive collaboration mechanism. First, a resource modeling framework for state coordination (RMFS) is developed to unify the representation of heterogeneous resources, their functionalities, and collaborative relationships through hybrid structural and semantic modeling. A resource state adaptive collaboration mechanism (RSACM) integrates multi-agent systems with knowledge graph to achieve real-time state synchronization. Agents utilize the collaborative relationships in the graph to make adaptive collaborative decisions on resource states, in order to perform state transitions and alleviate resource availability conflicts. Further, a meta-path-based resource inference (MPRI) method enables efficient resource retrieval and applies to simulation experiments by leveraging conceptual-instance meta-paths and large language model (LLM)-augmented substitution strategies to resolve resource unavailability. Experimental validation across emergency healthcare scenario demonstrates the framework’s effectiveness. An extension study was conducted on RMFS and RSACM through two cases from different fields. The proposed approach advances CPHS resource management by addressing heterogeneity, availability, and cooperativity in dynamic environments, offering theoretical and practical insights for complex system collaboration under spatiotemporal constraints.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"91 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Hierarchical reinforcement learning based on macro actions 基于宏观行为的分层强化学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01895-9

Hao Jiang, Gongju Wang, Shengze Li, Jieyuan Zhang, Long Yan, Xinhai Xu

{"title":"Hierarchical reinforcement learning based on macro actions","authors":"Hao Jiang, Gongju Wang, Shengze Li, Jieyuan Zhang, Long Yan, Xinhai Xu","doi":"10.1007/s40747-025-01895-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01895-9","url":null,"abstract":"The large action space is a key challenge in reinforcement learning. Although hierarchical methods have been proven to be effective in addressing this issue, they are not fully explored. This paper combines domain knowledge with hierarchical concepts to propose a novel Hierarchical Reinforcement Learning framework based on macro actions (HRL-MA). This framework includes a macro action mapping model that abstracts sequences of micro actions into macro actions, thereby simplifying the decision-making process. Macro actions are divided into two categories: combat macro actions (CMA) and non-combat macro actions (NO-CMA). NO-CMA are driven by decision tree-based logical rules and provide conditions for the execution of CMA. CMA form the action space of the reinforcement learning algorithm, which dynamically selects actions based on the current state. Comprehensive tests on the StarCraft II maps Simple64 and AbyssalReefLE demonstrate that the HRL-MA framework exhibits superior performance, achieving higher win rates compared to baseline algorithms. Furthermore, in mini-game scenarios, HRL-MA consistently outperforms baseline algorithms in terms of reward scores. The findings highlight the effectiveness of integrating hierarchical structures and macro actions in reinforcement learning to manage complex decision-making tasks in environments with large action spaces.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"17 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fleet formation identification and analyzing method based on disposition feature for remote sensing 基于遥感布局特征的舰队编队识别与分析方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01863-3

Fangli Mou, Zide Fan, Chuan’ao Jiang, Keqing Zhu, Lei Wang, Xinming Li

{"title":"Fleet formation identification and analyzing method based on disposition feature for remote sensing","authors":"Fangli Mou, Zide Fan, Chuan’ao Jiang, Keqing Zhu, Lei Wang, Xinming Li","doi":"10.1007/s40747-025-01863-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01863-3","url":null,"abstract":"Fleet formation identification in remote sensing is a significant focus in maritime surveillance. However, fleet may occur with different ship dense and noisy data due to the complex background and different satellite resolution, few studies have discussed formation identification considering the limits of sensing and application. This study introduces an effective fleet formation identification and analysis method based on disposition features for remote sensing. We fully consider and analyze detection performance in remote sensing applications, such as false alarms, missed detections, ship position errors and observed ship attitudes. A hierarchical density-based spatial clustering with noise method is introduced to cluster fleet regions. The robust disposition features are designed for various kinds of formations without using training data, enabling discrete remote sensing observations. Our method is robust to ship detection results and has low computational complexity, making it highly suitable for real applications. The advantages of our method were demonstrated through extensive experiments. The experimental results show that the proposed method achieves formation identification with an accuracy over 95% within less than 0.15 s when the ship detection performance changes over a large range, leading to a performance improvement of 5–27% compared with that of other comparative methods.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Jointly adaptive cross-resolution person re-identification on super-resolution 基于超分辨率的联合自适应跨分辨率人物再识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01881-1

Caihong Yuan, Zhijie Guan, Yuanchen Xu, Xiaopan Chen, Xiaoke Zhu, Wenjuan Liang

引用次数: 0

MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation MB-AGCL：推荐的多行为自适应图对比学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01880-2

Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen

{"title":"MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation","authors":"Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen","doi":"10.1007/s40747-025-01880-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01880-2","url":null,"abstract":"Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. In recent years, multi-behavior recommendation has addressed the challenges of data sparsity and cold start problems to some extent. However, the introduction of noise from multi-behavior tasks into the user-item graph exacerbates the impact of noise from a few active users and popularity bias from popular items. To tackle these challenges, graph augmentation has emerged as a promising approach in recommendation systems. However, existing augmentation methods may generate suboptimal graph structures, and maximizing correspondence may capture information unrelated to the recommendation task. To address these issues, we propose a novel approach called the Multi-Behavior Adaptive Graph Contrastive Learning Model (MB-AGCL) for recommendation. Our approach integrates auxiliary behaviors to compensate for data sparsity and utilizes adaptive learning to determine whether to drop edges or nodes, thus obtaining an optimized graph structure that reduces the impact of noise. We then train the original and generated graphs using supervised tasks. Furthermore, we propose an efficient adaptive graph augmentation method that integrates graph augmentation with down-stream tasks to reduce the impact of popularity bias. Finally, we jointly optimize these two tasks. Through extensive experiments on public datasets, we validate the effectiveness of our recommendation model.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"218 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention 通过多分支架构和混合域关注优化AlexNet的准确树种分类

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01836-6

Jianjianxian Liu, Tao Xing, Xiangyu Wang

{"title":"Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention","authors":"Jianjianxian Liu, Tao Xing, Xiangyu Wang","doi":"10.1007/s40747-025-01836-6","DOIUrl":"https://doi.org/10.1007/s40747-025-01836-6","url":null,"abstract":"Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come with the trade-off of significantly longer training times and higher computational costs. To address these challenges in tree species classification, an optimized AlexNet architecture, MMCAlexNet, is proposed to address these challenges for tree species classification. The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. The mixeddomain attention module enhances feature representation by focusing on critical spatial and channel-wise features. The joint loss function ensures better class separation and prevents overfitting by balancing classification accuracy and feature consistency. Experimental results demonstrate that MMCAlexNet outperforms the baseline AlexNet by 7.61%–9.69% in classification accuracy. Although the introduction of optimized structures increases computational complexity, it reduces the model size by 12MB. Furthermore, MMCAlexNet decreases the model size by 306.24MB and improves accuracy by 3.94%–6.35% compared to VGG16, demonstrating a balance between improved accuracy and computational efficiency.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"37 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Formal concept analysis assisted large-scale global optimization and its application to cloud task scheduling 形式化概念分析有助于大规模全局优化及其在云任务调度中的应用

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01878-w

Guo Yu, Yibo Yong, Chao Jiang, Fei Hao, Lianbo Ma

{"title":"Formal concept analysis assisted large-scale global optimization and its application to cloud task scheduling","authors":"Guo Yu, Yibo Yong, Chao Jiang, Fei Hao, Lianbo Ma","doi":"10.1007/s40747-025-01878-w","DOIUrl":"https://doi.org/10.1007/s40747-025-01878-w","url":null,"abstract":"Effective identification of interdependence information between decision variables is crucial for variable grouping in large-scale global optimization (LSGO). This paper introduces a novel approach called FCA-G (Formal Concept Analysis-Driven Grouping) to solve LSGO problems. FCA, an effective tool for data analysis, is employed in this approach. The primary contribution involves transforming decision variables into the formal context within FCA and utilizing the FCA methodology to solve LSGO problems based on a cooperative coevolution framework. Based on the formal context, a formal concept lattice is constructed, from which equivalent concepts are extracted. All variables within these concepts exhibit explicit interactions. This approach ensures a high degree of correlation among variables within subgroups and a low degree of correlation between subgroups, thereby enhancing cooperative coevolution. Experimental results indicate the significant potential of FCA-G in LSGO, as it outperforms state-of-the-art LSGO algorithms across the majority of LSGO test problems, including those with up to 1000 decision variables and a large-scale cloud task scheduling problem.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Attribute grouping-based categorical outlier detection using causal coupling weight 基于属性分组的基于因果耦合权的分类离群值检测

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-15 DOI: 10.1007/s40747-025-01869-x

Yijing Song, Jianying Liu, Jifu Zhang

{"title":"Attribute grouping-based categorical outlier detection using causal coupling weight","authors":"Yijing Song, Jianying Liu, Jifu Zhang","doi":"10.1007/s40747-025-01869-x","DOIUrl":"https://doi.org/10.1007/s40747-025-01869-x","url":null,"abstract":"For high-dimensional datasets, outlier objects can be effectively identified and extracted with the help of the coupling relationship between any two attributes. However, when all the coupling is used directly, there is a phenomenon of pseudo-correlation between attribute values that results in redundant coupling and affects the effectiveness of high-dimensional outlier detection. In this paper, a novel attribute group-based outlier detection approach for categorical data is proposed by using the attribute causal coupling weights to depict abnormal degree of the attributes. Firstly, according to the local and global correlation, all attributes are automatically divided into several groups, and all attributes in each group have a high correlation or association. Secondly, new concepts of causal pseudo-correlation are defined, and a case analysis that the pseudo-correlation is the main cause of attribute redundant coupling. By constructing attribute causality graph using the graph structure, the pseudo-correlation is effectively avoided in each attribute group. Thirdly, attribute causal coupling weight formula, which effectively characterizes the abnormal degree of attribute and reflects the causal coupling between any two attributes, is constructed from the causality graph. An attribute group-based outlier detection algorithm powered by causal coupling weight is proposed for categorical data. In the end, experimental results on the UCI and synthetic datasets validate that the algorithm has good outlier detection performance and effectively alleviates the effect of redundant coupling among attributes. Importantly, compared with the competitive methods, the algorithm bolsters the AUC index and the detection efficiency by averages of 10.97 and 42.84(%), respectively.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images WTI-SLAM：一种针对弱纹理热红外图像的红外视觉SLAM算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-15 DOI: 10.1007/s40747-025-01858-0

Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li

{"title":"WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images","authors":"Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li","doi":"10.1007/s40747-025-01858-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01858-0","url":null,"abstract":"This study addresses the challenges of robotic localization and navigation in visually degraded environments, such as low illumination and adverse weather conditions, by proposing a novel thermal infrared visual SLAM (Simultaneous Localization and Mapping) algorithm. The research introduces a new infrared visual odometry that integrates feature-based methods with optical flow techniques, enhancing image processing capabilities to mitigate the issues of high time overhead and cumulative errors in traditional feature-based odometry. Additionally, an improved bag-of-words model is employed to develop a novel loop closure detection method that addresses the challenge of scale drift. The purpose of this paper is to address the shortcomings in robustness and accuracy encountered by existing visual SLAM algorithms when processing low-texture thermal infrared images. Experimental validation using the JPL, Airey, and ViViD++ thermal infrared datasets demonstrates that the proposed algorithm exhibits superior real-time performance and robustness across various environments. Compared to mainstream thermal infrared visual SLAM algorithms, WTI-SLAM significantly improves the robot localization accuracy in weak-texture thermal infrared image scenarios, reducing the localization error by approximately 46%. This research offers an innovative and effective solution for achieving stable SLAM systems for robots operating in complex and visually degraded environments.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"74 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration 基于多模态分层语义引导深度融合和光流驱动特征集成的微表情识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01855-3

Haolin Chang, Zhihua Xie, Fan Yang

{"title":"Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration","authors":"Haolin Chang, Zhihua Xie, Fan Yang","doi":"10.1007/s40747-025-01855-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01855-3","url":null,"abstract":"Micro-expression (ME), as an involuntary and brief facial expression, holds significant potential applications in fields such as political psychology, lie detection, law enforcement, and healthcare. Most existing micro-expression spotting (MES) methods predominantly learn from optical flow features while neglecting the detailed information contained in RGB images. To address this issue, this paper proposes a multi-scale hierarchical semantic-guided end-to-end multimodal fusion framework based on Convolutional Neural Network (CNN)-Transformer for MES, named MESFusion. Specifically, to obtain cross-modal complementary information, this scheme sequentially constructs a Multi-Scale Feature Extraction Module (MFEM) and a Multi-scale hierarchical Semantic-Guided Fusion Module (MSGFM). By introducing an Optical Flow-Driven fusion feature Integration Module (OF-DIM), the correlation of non-scale fusion features is modeled in the channel dimension. Moreover, guided by the optical flow motion information, this approach can adaptively focus on facial motion areas and filter out interference information in cross-modal fusion. Extensive experiments conducted on the CAS(ME)2 dataset and the SAMM Long Videos dataset demonstrate that the MESFusion model surpasses competitive baselines and achieves new state-of-the-art results.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"39 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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