Complex & Intelligent Systems最新文献_第7页

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

Spatiotemporal decoupling attention transformer for 3D skeleton-based driver action recognition

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01811-1

Zhuoyan Xu, Jingke Xu

{"title":"Spatiotemporal decoupling attention transformer for 3D skeleton-based driver action recognition","authors":"Zhuoyan Xu, Jingke Xu","doi":"10.1007/s40747-025-01811-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01811-1","url":null,"abstract":"Driver action recognition is crucial for in-vehicle safety. We argue that the following factors limit the related research. First, spatial constraints and obstructions in the vehicle restrict the range of motion, resulting in similar action patterns and difficulty collecting the full body posture. Second, in skeleton-based action recognition, establishing the joint dependencies by the self-attention computation is always limited to a single frame, ignoring the effect of body spatial structure on dependence weights and inter-frame. Common convolution in temporal flow only focuses on frame-level temporal features, ignoring motion pattern features at a higher semantic level. Our work proposed a novel spatiotemporal decoupling attention transformer (SDA-TR). The SDA module uses a spatiotemporal decoupling strategy to decouple the weight computation according to body structure and directly establish joint dependencies between multiple frames. The TFA module aggregates sub-action-level and frame-level temporal features to improve similar recognition accuracy. On the Driver Action Recognition dataset Drive&Act using driver upper body skeletons, SDA-TR achieves state-of-the-art performance. SDA-TR also achieved 92.2%/95.8% accuracy under the CS/CV benchmarks of NTU RGB+D 60, 88.6%/89.8% accuracy under the CS/CSet benchmarks of NTU RGB+D 120, on par with other state-of-the-art methods. Our method demonstrates great scalability and generalization for action recognition.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"32 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518626","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

BSAformer: bidirectional sequence splitting aggregation attention mechanism for long term series forecasting

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-02-28 DOI: 10.1007/s40747-025-01794-z

QingBo Zhu, JiaLin Han, Sheng Yang, ZhiQiang Xie, Bo Tian, HaiBo Wan, Kai Chai

{"title":"BSAformer: bidirectional sequence splitting aggregation attention mechanism for long term series forecasting","authors":"QingBo Zhu, JiaLin Han, Sheng Yang, ZhiQiang Xie, Bo Tian, HaiBo Wan, Kai Chai","doi":"10.1007/s40747-025-01794-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01794-z","url":null,"abstract":"Time series forecasting plays a crucial role across various sectors, including energy, transportation, meteorology, and epidemiology. However, existing models often struggle with capturing long-term dependencies and managing computational efficiency when handling complex and extensive time series data. To address these challenges, this paper introduces the BSAformer model, which leverages a unique combination of frequency-domain Sequence Progressive Split-Aggregation (SPSA) and Bidirectional Splitting-Agg Attention (BSAA) mechanisms. The SPSA module decomposes sequences into seasonal and trend components, enhancing the model’s ability to identify cyclical patterns, while the BSAA mechanism captures forward and backward dependencies, providing a comprehensive understanding of temporal dynamics. Extensive experiments conducted on seven benchmark datasets demonstrate the BSAformer model's superior performance, with notable improvements in accuracy and efficiency over state-of-the-art models. Specifically, the BSAformer achieves significant Mean Squared Error (MSE) reductions of 63.7% on the ECL dataset, 28.1% on the Traffic dataset, and 49.8% on the ILI dataset. These results validate the model’s robustness and its adaptability across diverse time series forecasting scenarios. The insights gained from this study contribute to the advancement of time series forecasting by providing a model that improves both accuracy and computational efficiency, especially in handling long-term dependencies and complex temporal patterns. ","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518616","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