Complex & Intelligent Systems最新文献_第2页

Demonstration and offset augmented meta reinforcement learning with sparse rewards

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01785-0

Haorui Li, Jiaqi Liang, Xiaoxuan Wang, Chengzhi Jiang, Linjing Li, Daniel Zeng

{"title":"Demonstration and offset augmented meta reinforcement learning with sparse rewards","authors":"Haorui Li, Jiaqi Liang, Xiaoxuan Wang, Chengzhi Jiang, Linjing Li, Daniel Zeng","doi":"10.1007/s40747-025-01785-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01785-0","url":null,"abstract":"This paper introduces DOAMRL, a novel meta-reinforcement learning (meta-RL) method that extends the Model-Agnostic Meta-Learning (MAML) framework. The method addresses a key limitation of existing meta-RL approaches, which struggle to effectively use suboptimal demonstrations to guide training in sparse reward environments. DOAMRL effectively combines reinforcement learning (RL) and imitation learning (IL) within the inner loop of the MAML framework, with dynamically adjusted weights applied to the IL component. This enables the method to leverage the exploration strengths of RL and the efficiency benefits of IL at different stages of training. Additionally, DOAMRL introduces a meta-learned parameter offset, which enhances targeted exploration in sparse reward settings, helping to guide the meta-policy toward regions with non-zero rewards. To further mitigate the impact of suboptimal demonstration data on meta-training, we propose a novel demonstration data enhancement module that iteratively improves the quality of the demonstrations. We provide a comprehensive analysis of the proposed method, justifying its design choices. A comprehensive comparison with existing methods in various stages (including training and adaptation), using both optimal and suboptimal demonstrations, along with results from ablation and sensitivity analysis, demonstrates that DOAMRL outperforms existing approaches in performance, applicability, and robustness.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"71 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518625","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

Design of weighted based divided-search enhanced Karnik–Mendel algorithms for type reduction of general type-2 fuzzy logic systems

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01798-9

Yang Chen

引用次数: 0

Exact particle flow Daum-Huang filters for mobile robot localization in occupancy grid maps

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01810-2

Domonkos Csuzdi, Tamás Bécsi, Péter Gáspár, Olivér Törő

{"title":"Exact particle flow Daum-Huang filters for mobile robot localization in occupancy grid maps","authors":"Domonkos Csuzdi, Tamás Bécsi, Péter Gáspár, Olivér Törő","doi":"10.1007/s40747-025-01810-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01810-2","url":null,"abstract":"In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities of modern filtering techniques for this task. Building on a recent localization method that employs a chamfer distance-based observation model, derived from an implicit measurement equation, we explore its potential further by incorporating exact particle flow Daum–Huang filters to achieve superior accuracy. Recent advancements have spotlighted Daum–Huang filters as formidable contenders, outshining both the extended Kalman filters and traditional particle filters in various scenarios. We introduce two new Daum–Huang-based localization algorithms and assess their tracking performance through comprehensive simulations and real-world trials. Our algorithms are benchmarked against various methods, including the widely acclaimed Adaptive Monte–Carlo Localization algorithm. Overall, our algorithm demonstrates superior performance compared to the baseline models in simulations and exhibits competitive performance in the evaluated real-world application.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518620","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

Unsupervised random walk manifold contrastive hashing for multimedia retrieval

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01814-y

Yunfei Chen, Yitian Long, Zhan Yang, Jun Long

{"title":"Unsupervised random walk manifold contrastive hashing for multimedia retrieval","authors":"Yunfei Chen, Yitian Long, Zhan Yang, Jun Long","doi":"10.1007/s40747-025-01814-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01814-y","url":null,"abstract":"With the rapid growth in both the variety and volume of data on networks, especially within social networks containing vast multimedia data such as text, images, and video, there is an urgent need for efficient methods to retrieve helpful information quickly. Due to their high computational efficiency and low storage costs, unsupervised deep cross-modal hashing methods have become the primary method for managing large-scale multimedia data. However, existing unsupervised deep cross-modal hashing methods still need help with issues such as inaccurate measurement of semantic similarity information, complex network architectures, and incomplete constraints among multimedia data. To address these issues, we propose an Unsupervised Random Walk Manifold Contrastive Hashing (URWMCH) method, designing a simple deep learning architecture. First, we build a random walk-based manifold similarity matrix based on the random walk strategy and modal-individual similarity structure. Second, we construct intra- and inter-modal similarity preservation and coexistent similarity preservation loss based on contrastive learning to constrain the training of hash functions, ensuring that the hash codes contain complete semantic association information. Finally, we designed comprehensive experiments on the MIRFlickr-25K, NUS-WIDE, and MS COCO datasets to demonstrate the effectiveness and superiority of the proposed URWMCH method.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518811","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

A model of feature extraction for well logging data based on graph regularized non-negative matrix factorization with optimal estimation

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01783-2

Kehong Yuan, Youlin Shang, Haixiang Guo, Yongsheng Dong, Zhonghua Liu

{"title":"A model of feature extraction for well logging data based on graph regularized non-negative matrix factorization with optimal estimation","authors":"Kehong Yuan, Youlin Shang, Haixiang Guo, Yongsheng Dong, Zhonghua Liu","doi":"10.1007/s40747-025-01783-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01783-2","url":null,"abstract":"Reservoir oil-bearing recognition is the process of predicting reservoir types based on well logging data, which determines the accuracy of recognition. However, the original well logging data is multidimensional and contains potential noise, which can influence the performance of sequent processing, such as clustering and classification. It is crucial to obtain key low-dimensional features and study an accurate automatic recognition algorithm under unsupervised condition. To solve this problem, we propose a feature extraction method named graph regularized non-negative matrix factorization with optimal estimation (GNMF-OE) according to the characteristics of well logging data in this paper. Firstly, the low dimensional embedding dimension of high-dimensional well logging data is modeled and estimated, which enables the method to obtain the appropriate number of features that reflect the data structure. Secondly, local features are optimized by structured initial vectors in the framework of GNMF, which encourages the basis matrix to have clear reservoir category characteristics. These two approaches are meaningful and beneficial to construct an appropriate basis matrix that discovers the intrinsic structure of well logging data. The visualized experimental results on real datasets from Jianghan oilfield in China show that the proposed method has significant clustering performance for reservoir oil-bearing recognition.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"49 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518808","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

A novel robust multi-objective evolutionary optimization algorithm based on surviving rate

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01822-y

Wenxiang Jiang, Kai Gao, Shuwei Zhu, Lihong Xu

{"title":"A novel robust multi-objective evolutionary optimization algorithm based on surviving rate","authors":"Wenxiang Jiang, Kai Gao, Shuwei Zhu, Lihong Xu","doi":"10.1007/s40747-025-01822-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01822-y","url":null,"abstract":"Multi-objective evolutionary optimization is widely utilized in industrial design. Despite the success of multi-objective evolutionary optimization algorithms in addressing complex optimization problems, research focusing on input disturbances remains limited. In many manufacturing processes, design parameters are vulnerable to random input disturbances, resulting in products that often perform less effectively than anticipated. To address this issue, we propose a novel robust multi-objective evolutionary optimization algorithm based on the concept of survival rate. The algorithm comprises two stages: the evolutionary optimization stage and the construction stage of the robust optimal front. In the former stage, we introduce the survival rate as a new optimization objective. Subsequently, we seek a robust optimal front that concurrently addresses convergence and robustness by employing a non-dominated sorting approach. Furthermore, we propose a precise sampling method and a random grouping mechanism to accurately recover solutions resilient to real noise while ensuring population’s diversity. In the latter stage, we introduce a performance measure that integrates both robustness and convergence to guide the construction of the robust optimal front. Experimental results demonstrate the superiority of the proposed algorithm in terms of both convergence and robustness compared to existing approaches under noisy conditions.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"52 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518622","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

ConvNeXt embedded U-Net for semantic segmentation in urban scenes of multi-scale targets

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-024-01735-2

Yanyan Wu, Qian Li

{"title":"ConvNeXt embedded U-Net for semantic segmentation in urban scenes of multi-scale targets","authors":"Yanyan Wu, Qian Li","doi":"10.1007/s40747-024-01735-2","DOIUrl":"https://doi.org/10.1007/s40747-024-01735-2","url":null,"abstract":"Semantic segmentation of urban scenes is essential in urban traffic analysis and road condition information acquisition. The semantic segmentation model with good performance is the key to applying high-resolution urban locations. However, the types of these images are diverse, and the spatial relationships are complex. It is greatly affected by weather and light. Objects of different scales pose significant challenges to image segmentation of urban scenes. The existing semantic segmentation is mostly solved from the target scale and superpixel methods. Our research mainly fills the gap in image segmentation field of ConvNeXt fusion U-Net pyramid network model in specific urban scenes. These methods could be more accurate. Therefore, we propose the multi-scale fusion deformation residual pyramid network model method in this paper. This method captures features of different scales and effectively solves the problem of urban scene image segmentation of memory scenes by objects of different scales. We construct a spatial information interaction module to reduce the semantic ambiguity caused by complex spatial relations. By combining spatial and channel characteristics, a series of problems caused by weather and light can be alleviated. We verify the improved semantic segmentation model on the Cityscape dataset. The experimental results show that the method achieves 84.25% MPA and 75.61% MIoU. Our improved algorithm, ConvNeXt embedding in the U-Net algorithm architecture, is named Conv-UNet. The improved method proposed in this paper is superior to other methods in the semantic segmentation of urban scenes. The main advantage of this algorithm is to explore the specific loss function and segmentation strategy suitable for urban scene in the face of the complexity and diversity of urban scene images.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518640","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

Pattern mining-based evolutionary multi-objective algorithm for beam angle optimization in intensity-modulated radiotherapy

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01809-9

Ruifen Cao, Wei Chen, Tielu Zhang, Langchun Si, Xi Pei, Xingyi Zhang

{"title":"Pattern mining-based evolutionary multi-objective algorithm for beam angle optimization in intensity-modulated radiotherapy","authors":"Ruifen Cao, Wei Chen, Tielu Zhang, Langchun Si, Xi Pei, Xingyi Zhang","doi":"10.1007/s40747-025-01809-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01809-9","url":null,"abstract":"Evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to the ignorance of using the specific clinical knowledge that can be obtain intuitively by clinical physicist. To address this issue, we suggest a pattern mining based evolutionary multi-objective algorithm called PM-EMA, in which two strategies for using the knowledge are proposed to accelerate the speed of population convergence. Firstly, to discover the potential beam angle distribution and discard the worse angles, the pattern mining strategy is used to detect the maximum and minimum sets of beam angles in non-dominated solutions of the population and utilize them to generate offspring to enhance the convergence. Moreover, to improve the quality of initial solutions, a tailored population initialization strategy is proposed by using the score of beam angles defined by this study. The experimental results on six clinical cancer cases demonstrate the superior performance of the proposed algorithm over six representative algorithms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"52 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518810","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

KeyBoxGAN: enhancing 2D object detection through annotated and editable image synthesis

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01817-9

Yashuo Bai, Yong Song, Fei Dong, Xu Li, Ya Zhou, Yizhao Liao, Jinxiang Huang, Xin Yang

{"title":"KeyBoxGAN: enhancing 2D object detection through annotated and editable image synthesis","authors":"Yashuo Bai, Yong Song, Fei Dong, Xu Li, Ya Zhou, Yizhao Liao, Jinxiang Huang, Xin Yang","doi":"10.1007/s40747-025-01817-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01817-9","url":null,"abstract":"Sample augmentation, especially sample generation is conducive for addressing the challenge of training robust image and video object detection models based on the deep learning. Still, the existing methods lack sample editing capability and suffer from annotation work. This paper proposes an image sample generation method based on key box points detection and Generative adversarial network (GAN), named as KeyBoxGAN, to make image sample generation labeled and editable. KeyBoxGAN firstly predefines key box points positions, embeddings which control the objects’ positions and then the corresponding masks are generated according to Mahalanobis–Gaussuan heatmaps and Swin Transformer-SPADE generator to control objects’ generation regions, as well as the background generation. This adaptive and precisely supervised image generation method disentangles object position and appearance, enables image editable and self-labeled abilities. The experiments show KeyBoxGAN surpasses DCGAN, StyleGAN2 and DDPM in objective assessments, including Inception Distance (FID), Inception Score (IS), and Multi-Scale Structural Similarity Index (MS-SSIM), as well as in subjective evaluations by showing better visual quality. Moreover, the editable and self-labeled image generation capabilities make it a valuable tool in addressing challenges like occlusion, deformation, and varying environmental conditions in the 2D object detection.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"66 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518619","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

ADWTune: an adaptive dynamic workload tuning system with deep reinforcement learning

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01801-3

Cuixia Li, Junhai Wang, Jiahao Shi, Liqiang Liu, Shuyan Zhang

引用次数: 0