Complex & Intelligent Systems最新文献_第6页

CPP: a path planning method taking into account obstacle shadow hiding CPP：一种考虑障碍物阴影隐藏的路径规划方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01718-3

Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang

{"title":"CPP: a path planning method taking into account obstacle shadow hiding","authors":"Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang","doi":"10.1007/s40747-024-01718-3","DOIUrl":"https://doi.org/10.1007/s40747-024-01718-3","url":null,"abstract":"Path planning algorithms are crucial for the autonomous navigation and task execution of unmanned vehicles in battlefield environments. However, existing path planning algorithms often overlook the concealment effects of obstacles, which can lead to significant safety risks for unmanned vehicles during operation. To address this issue, we proposed a novel path planning method—Covert Path Planning (CPP)—that incorporated considerations for the shadow occlusion caused by obstacles. By accounting for these concealment effects, CPP aimed to enhance the safety and effectiveness of unmanned vehicles in complex and dynamic battlefield scenarios. It started by designing shadow areas in the configuration environment based on solar azimuth and altitude angles. A gravitational field model was then created using these shadow areas and the target point’s position to guide the path point movement, achieving a path with a higher safety coefficient. The method also dynamically adjusted step length according to gravitational forces to boost planning efficiency. Additionally, a deformed ellipse-based obstacle avoidance technique was introduced to enhance the vehicle’s ability to navigate around obstacles. We simplified the path by considering the relationship between path points and shadows. We also proposed a Minimum-Jerk Trajectory Optimization method with controllable path noise points, which enhanced path smoothness and reduced predictability. Comparative analysis showed that CPP significantly outperformed five other algorithms—RRT, Improved B-RRT, RRT*, Informed RRT*, and Potential Field-by reducing running time by 46.01% to 93.3%, increasing path safety by 10.42% to 83.44%, and improving path smoothness, making it particularly effective for path planning in tactical scenarios involving unmanned vehicles.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"161 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934910","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

New Jensen–Shannon divergence measures for intuitionistic fuzzy sets with the construction of a parametric intuitionistic fuzzy TOPSIS 基于参数直觉模糊TOPSIS构造直觉模糊集的新的Jensen-Shannon散度测度

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-07 DOI: 10.1007/s40747-024-01761-0

Xinxing Wu, Qian Liu, Lantian Liu, Miin-Shen Yang, Xu Zhang

{"title":"New Jensen–Shannon divergence measures for intuitionistic fuzzy sets with the construction of a parametric intuitionistic fuzzy TOPSIS","authors":"Xinxing Wu, Qian Liu, Lantian Liu, Miin-Shen Yang, Xu Zhang","doi":"10.1007/s40747-024-01761-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01761-0","url":null,"abstract":"In this paper, we first give an example to show that Theorem 1 in Hung and Yang (Inf Sci 178(6):1641–1650, 2008) does not hold, implying that the J-divergence introduced by Hung and Yang does not satisfy the axiomatic definition of intuitionistic fuzzy divergence measure. Inspired by this, a new Jensen–Shannon divergence measure for intuitionistic fuzzy sets (IFSs) is introduced and some basic properties for this new divergence measure are obtained. In particular, this divergence measure, and its induced similarity measure, and induced entropy measure satisfy the axiomatic definitions of divergence, similarity, and entropy for IFSs. Based on our proposed divergence measure, entropy measure, and entropy-weight method, a new TOPSIS method is introduced to deal with multi-attribute decision making (MADM) problems under the intuitionistic fuzzy framework. Finally, a practical example on the credit evaluation of potential strategic partners and a comparative analysis with other TOPSIS methods is developed to illustrate the efficiency of the proposed TOPSIS method.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"342 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934952","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 quadratic $$nu $$ -support vector regression approach for load forecasting 负荷预测的二次$$nu $$ -支持向量回归方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01730-7

Yanhe Jia, Shuaiguang Zhou, Yiwen Wang, Fengming Lin, Zheming Gao

引用次数: 0

Unleashing the power of pinyin: promoting Chinese named entity recognition with multiple embedding and attention 释放拼音的力量：以多重嵌入和关注推动中文命名实体识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01753-0

Jigui Zhao, Yurong Qian, Shuxiang Hou, Jiayin Chen, Kui Wang, Min Liu, Aizimaiti Xiaokaiti

{"title":"Unleashing the power of pinyin: promoting Chinese named entity recognition with multiple embedding and attention","authors":"Jigui Zhao, Yurong Qian, Shuxiang Hou, Jiayin Chen, Kui Wang, Min Liu, Aizimaiti Xiaokaiti","doi":"10.1007/s40747-024-01753-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01753-0","url":null,"abstract":"Named Entity Recognition (NER) aims to identify entities with specific meanings and their boundaries in natural language texts. Due to the differences between Chinese and English language families, Chinese NER faces challenges such as ambiguous word boundary delineation and semantic diversity. Previous studies on Chinese NER have focused on character and lexical information, neglecting the unique feature of Chinese—pinyin information. In this paper, we propose CPL-NER, which combines multiple feature information of Chinese characters as embedding to enhance the semantic representation by introducing pinyin and dictionary information. For Chinese named entity recognition, pinyin information of Chinese characters helps to resolve the polyphonic phenomenon, while dictionary information aids in addressing word segmentation ambiguities. Additionally, we innovatively designed the Pinyin-Lexicon Cross-Attention Mechanism (PLCA), which calculates attention scores between various embeddings. This mechanism deeply integrates character, pinyin, and lexicon embeddings, generating character sequences enriched with semantic information. Finally, BiLSTM-CRF is employed for sequence modeling. Through this design, we can more comprehensively capture semantic features in Chinese text, improving the model’s ability to handle polyphonic characters and word segmentation ambiguities, thereby enhancing the recognition performance of Chinese named entities. We conducted experiments on four standard Chinese NER benchmark datasets, and the results show that our method outperforms most baselines, demonstrating the effectiveness of our proposed model.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"34 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924663","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

An interaction relational inference method for a coal-mining equipment system 煤矿设备系统的交互关系推理方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01765-w

Xiangang Cao, Jiajun Gao, Xin Yang, Fuyuan Zhao, Boyang Cheng

{"title":"An interaction relational inference method for a coal-mining equipment system","authors":"Xiangang Cao, Jiajun Gao, Xin Yang, Fuyuan Zhao, Boyang Cheng","doi":"10.1007/s40747-024-01765-w","DOIUrl":"https://doi.org/10.1007/s40747-024-01765-w","url":null,"abstract":"Multiple potential interactions occur in a coal-mining equipment system during operation, which is crucial for understanding and predicting the dynamic system evolution. Existing methods for building interaction relations in coal-mining equipment systems face problems including incomplete selection of system nodes and difficulty in defining interaction-relation types and distinguishing interaction-relation weights. This study proposes an interaction-relation inference method EMIFC-CIRI for coal-mining equipment systems. EMIFC-CIRI first builds a monitoring index system for coal-mining equipment based on evidence and then accurately selects system nodes. The interaction constructor of the CIRI interaction inference model in this method introduces Gumbel-softmax technology, which autonomously generates multiple types of interaction relations based on several probability matrices. CIRI’s interaction optimizer introduces an attention mechanism to assign weights to interaction relations, and it predicts future system states based on device-monitoring data and interaction relations, optimizing the types and weights of interaction relations between nodes by reducing prediction errors. The study included experiments on relevant datasets. The results show that EMIFC-CIRI successfully built various interaction relations of different strengths, with a 156.17% improvement in interaction-relation quality and a 68.17% improvement in dynamic modeling performance compared with state-of-the-art comparison methods. This study provides a new perspective for research in the field of interaction reasoning of coal-mining equipment systems.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"19 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924662","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

Causal discovery and fault diagnosis based on mixed data types for system reliability modeling 基于混合数据类型的系统可靠性建模的原因发现和故障诊断

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01740-5

Xiaokang Wang, Siqi Jiang, Xinghan Li, Mozhu Wang

{"title":"Causal discovery and fault diagnosis based on mixed data types for system reliability modeling","authors":"Xiaokang Wang, Siqi Jiang, Xinghan Li, Mozhu Wang","doi":"10.1007/s40747-024-01740-5","DOIUrl":"https://doi.org/10.1007/s40747-024-01740-5","url":null,"abstract":"Causal relationships play an irreplaceable role in revealing the mechanisms of phenomena and guiding intervention actions. However, due to limitations in existing frameworks regarding model representations and learning algorithms, only a few studies have explored causal discovery on non-Euclidean data. In this paper, we address the issue by proposing a causal mapping process based on coordinate representations for heterogeneous non-Euclidean data. We propose a data generation mechanism between the parent nodes and the child nodes and create a causal mechanism based on multi-dimensional tensor regression. Furthermore, within the aforementioned theoretical framework, we propose a two-stage causal discovery approach based on regularized generalized canonical correlation analysis. Using the discrete representation in the shared projection direction, causal relationships between heterogeneous non-Euclidean variables can be discovered more accurately. Finally, empirical research is conducted on real-world industrial sensor data, which demonstrates the effectiveness of the proposed method for discovering causal relationships in heterogeneous non-Euclidean data.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"79 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924736","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 graph convolution and frequency domain filtering approach for hyperspectral anomaly detection 一种新的图卷积和频域滤波方法用于高光谱异常检测

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01738-z

Yang Ding, Hao Yan, Jingyuan He, Juanjuan Yin, A. Ruhan

{"title":"A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection","authors":"Yang Ding, Hao Yan, Jingyuan He, Juanjuan Yin, A. Ruhan","doi":"10.1007/s40747-024-01738-z","DOIUrl":"https://doi.org/10.1007/s40747-024-01738-z","url":null,"abstract":"This paper introduces a novel algorithm for hyperspectral anomaly detection (HAD) that combines graph-based representations with frequency domain filtering techniques. In this approach, hyperspectral images (HSIs) are modeled as graphs, where each pixel is treated as a node with spectral features, and the edges capture pixel correlations based on the K-Nearest Neighbor (KNN) algorithm. Graph convolution is employed to extract spatial structural features, enhancing the understanding of spatial relationships within the data. Additionally, the algorithm addresses the ’right-shift’ phenomenon in the spectral domain, often associated with anomalies, by using a beta wavelet filter for efficient spectral filtering and anomaly detection. The key contributions of this work include: 1) the use of a graph-based model for HSI that effectively integrates both spatial and spectral dimensions, 2) employing KNN for edge construction to include distant pixels and mitigate noise, 3) spatial feature extraction via graph convolution to provide detailed insights into spatial interconnections and variations, enhancing the detection process, and 4) leveraging the beta wavelet filter to handle the ’right-shift’ spectral phenomenon and reduce computational complexity. Experimental evaluations on four benchmark datasets show that the proposed method achieves outstanding performance with AUC scores of 0.9986, 0.9975, 0.9859, and 0.9988, significantly outperforming traditional and state-of-the-art anomaly detection techniques.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"73 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924656","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

CSTrans: cross-subdomain transformer for unsupervised domain adaptation CSTrans：用于无监督域自适应的跨子域变压器

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01709-4

Junchi Liu, Xiang Zhang, Zhigang Luo

{"title":"CSTrans: cross-subdomain transformer for unsupervised domain adaptation","authors":"Junchi Liu, Xiang Zhang, Zhigang Luo","doi":"10.1007/s40747-024-01709-4","DOIUrl":"https://doi.org/10.1007/s40747-024-01709-4","url":null,"abstract":"Unsupervised domain adaptation (UDA) aims to make full use of a labeled source domain data to classify an unlabeled target domain data. With the success of Transformer in various vision tasks, existing UDA methods borrow strong Transformer framework to learn global domain-invariant feature representation from the domain level or category level. Of them, the cross-attention as a key component acts for the cross-domain feature alignment, benefiting from its robustness. Intriguingly, we find that the robustness makes the model insensitive to the sub-grouping property within the same category of both source and target domains, known as the subdomain structure. This is because the robustness regards some fine-grained information as the noises and removes them. To overcome this shortcoming, we propose an end-to-end Cross-Subdomain Transformer framework (CSTrans) to exploit the transferability of subdomain structures and the robustness of cross-attention to calibrate inter-domain features. Specifically, there are two innovations in this paper. First, we devise an efficient Index Matching Module (IMM) to calculate the cross-attention of the same category in different domains and learn the domain-invariant representation. This not only simplifies the traditional daunting image-pair selection but also paves the safer way for guarding fine-grained subdomain information. This is because the IMM implements reliable feature confusion. Second, we introduce discriminative clustering to mine the subdomain structures in the same category and further learn subdomain discrimination. Both aspects cooperates with each other for fewer training stages. We perform extensive studies on five benchmarks, and the respective experimental results show that, as compared to existing UDA siblings, CSTrans attains remarkable results with average classification accuracy of 94.3%, 92.1%, and 85.4% on datasets Office-31, ImageCLEF-DA, and Office-Home, respectively.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924739","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

IMTLM-Net: improved multi-task transformer based on localization mechanism network for handwritten English text recognition IMTLM-Net：改进的基于定位机制网络的多任务转换器，用于手写体英语文本识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-04 DOI: 10.1007/s40747-024-01713-8

Qianfeng Zhang, Feng Liu, Wanru Song

{"title":"IMTLM-Net: improved multi-task transformer based on localization mechanism network for handwritten English text recognition","authors":"Qianfeng Zhang, Feng Liu, Wanru Song","doi":"10.1007/s40747-024-01713-8","DOIUrl":"https://doi.org/10.1007/s40747-024-01713-8","url":null,"abstract":"Intelligence technology has widely empowered education. As an example, Optical Character Recognition (OCR) can be used in smart education scenarios such as online homework correction and teaching data analysis. One of the fundamental yet challenging tasks is to recognize images of handwritten English text as editable text accurately. This is because handwritten text tends to have different writing habits as well as smearing and overlapping, resulting in hard alignment between the image and the real text. Additionally, the lack of data on handwritten text further leads to a lower recognition rate. To address the above issue, on the one hand, this paper extends the existing dataset and introduces hyphenated data annotation to provide data support for improving the robustness and discrimination of the model; on the other hand, a novel framework named Improved Multi-task Transformer based on Localization Mechanism Network (IMTLM-Net) is proposed for handwritten English text recognition. IMTLM-Net contains two parts, namely the encoding and decoding modules. The encoding module introduces a dual-stream processing mechanism. That is, in the simultaneous processing of text and images, a Vision Transformer (VIT) is utilized to encode images, and a Permutation Language Model (PLM) is designed for word arrangement. Two Multiple Head Attention (MHA) units are employed in the decoding module, focusing on text sequences and image sequences. Moreover, the localization mechanism (LM) is applied to enhance font structure feature extraction from image data, which in turn improves the model’s ability to capture complex details. Numerous experiments demonstrate that the proposed method achieves state-of-the-art results in handwritten text recognition.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"5 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924737","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

Graph attention, learning 2-opt algorithm for the traveling salesman problem 图注意，学习2-opt算法求解旅行商问题

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-03 DOI: 10.1007/s40747-024-01716-5

Jia Luo, Herui Heng, Geng Wu

引用次数: 0