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Regional Constraint Module-Based Multi-Agent Path Planning Approach for Car-like Agents

Journal of Shanghai Jiaotong university (science) 2024年 1-11页

作者： Fang, Chengyuan Mao, Jianlin Li, Dayan Wang, Ning Wang, Niya Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Multi-agent path finding (MAPF) is a challenging problem widely employed in automated docks and warehouse systems. However, when the above scenarios require car-like agents to perform the tasks, due to the complexity of the environment and the specificity of the shape of the agents, numerous conflicts between agents may occur in the process of path planning, which seriously affects the efficiency of the system and leads to a long runtime. To address these above problems, we propose a regional constraint module-based car-like conflict-based search (RCM-CL-CBS), which sets up the safe region to detect the conflicts between agents, maximizing the selection of paths with larger spatial resources under the same cost, and specifies the safe-exclusive region for colliding agents, reducing the probability of agents’ collisions within a certain region. We conduct experiments under four scenario types including factory and warehousing instances. Compared with the baseline algorithms, the experimental results denote that our method reduces the computational burden in terms of resolving agent conflicts, and improves the efficiency of problem-solving. In particular, in the warehouse scenario, compared to the car-like conflict-based search (CL-CBS), CL-CBS in the sequential framework (CL-CBS-SE), and improved CL-CBS (ICL-CBS), our method in the sequential framework minimizes the runtime by 89.6%, 53.4%, and 46.6%, respectively. (Figure presented.). © Shanghai Jiao Tong university 2024.

关键词： Motion planning

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DSFNet: Deep Fusion Parallel MRI Reconstruction Based on SPIRiT and Complex Convolution

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Journal of Shanghai Jiaotong university (science) 2024年 1-13页

作者： Duan, Jizhong Ren, Xinmin Chen, Shengyi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Long data acquisition time is an inherent disadvantage of magnetic resonance imaging (MRI). To accelerate the data acquisition speed of MRI, undersampling is required, which results in low imaging quality. Based on an iterative self-consistent parallel imaging reconstruction (SPIRiT) and improved complex convolutional neural networks, a deep SPIRiT fusion network (DSFNet) is proposed to improve the quality of reconstructed images. The DSFNet model first uses the SPIRiT model for the reconstruction of under-sampled k-space data. Subsequently, a cascaded complex convolutional neural network with dense connections is utilized for further reconstruction of the calibrated k-space data. Besides, a data consistency layer ensures the fidelity of the reconstructed image in both k-space and image domains. The final magnitude image is fused from the two parts of the reconstruction magnitude images by the sequentialized model-based Bayesian optimization fusion module at a certain ratio. Experimental results on different knee datasets show that DSFNet can bring about a substantial improvement in the visualization quality, peak signal-to-noise ratio and structural similarity of the reconstructed images compared to the SPIRiT, Deepcomplex and DONet models. (Figure presented.) © Shanghai Jiao Tong university 2024.

关键词： Signal to noise ratio

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CSC-YOLO: An Image Recognition Model for Surface Defect Detection of Copper Strip and Plates

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Journal of Shanghai Jiaotong university (science) 2024年 1-13页

作者： Zhang, Guo Chen, Tao Wang, Jianping Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production, a detection model of surface defects based on machine vision, CSC-YOLO, is proposed. The model uses YOLOv4-tiny as the benchmark network. First, K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset. Second, a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information. Third, the spatial pyramid pooling-efficient channel attention network (SPP-E) module is introduced in the path aggregation network (PANet) to enhance the extraction of features. Fourth, to prevent the loss of channel information, a lightweight attention mechanism is introduced to improve the performance of the network. Finally, the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects. CSC-YOLO was tested on the self-built dataset of surface defects in copper strip, and the experimental results showed that the mAP of the model can reach 93.58%, which is a 3.37% improvement compared with the benchmark network, and FPS, although decreasing compared with the benchmark network, reached 104. CSC-YOLO takes into account the real-time requirements of copper strip production. The comparison experiments with Faster RCNN, SSD300, YOLOv3, YOLOv4, Resnet50-YOLOv4, YOLOv5s, YOLOv7, and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy. © Shanghai Jiao Tong university 2024.

关键词： Surface defects

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GNNPPOR: A Proximal Policy Optimization Multi-Factor Joint Routing Approach Based on Graph Neural Networks in FANETs

IEEE Networking Letters

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IEEE Networking Letters 2025年第2期7卷 130-134页

作者： Song, Jian Li, Jing Wang, Qingwang Gu, Yebo Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China

Given the significant challenges of low resource utilization, load imbalance, and difficulties in meeting quality of service requirements in Flying Ad Hoc Networks (FANETs) routing protocols, this letter proposes a Graph Neural Network (GNN)-based approach for proximal policy optimization routing (GNNPPOR). The approach aims to integrate traffic engineering into FANETs to effectively distribute network load and meet quality of service requirements. In GNNPPOR, we design a GNN model that first aggregates multi-dimensional network state information efficiently through a message-passing mechanism. Subsequently, the network state is updated in real-time using a gated recurrent unit to adapt to dynamic changes in the FANETs network state. Finally, a multi-factor joint decision-making approach is proposed to identify suitable routes for each traffic based on the current network state. Simulation results demonstrate that GNNPPOR outperforms existing methods in several key metrics. Specifically, packet delivery rate increased by 25.3%, while energy consumption and network jitter decreased by 12.8% and 24.9%, respectively. © 2019 IEEE.

关键词： Graph neural networks

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A laser powered EO sensor for measurement of EMP

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Optoelectronics Letters 2024年第12期20卷 709-713页

作者： ZHANG Jiahong MA Chao WANG Zhuo Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China

This article presents an active electro-optic(EO)modulation electromagnetic pulse(EMP)sensor powered by *** sensor simulation model is established,and the temporal response characteristics of the sensor are analyzed based on finite element method(FEM).The laser powered supply circuit and receiving modulation circuit are designed and implemented.A monopole antenna is designed and used to revive the *** integrating the supply circuit,the receiving modulation circuit and the antenna together,the sensor is finally fabricated and *** results indicate that with the designed laser powered supply circuit,the sensor can operate continuously and *** measured standard 1.2/50μs lightning electromagnetic pulse(LEMP)in the time domain agrees well with the input LEMP voltage *** linear maximum and minimum measurable electric fields of the sensor are 20.8 kV/m and 221 V/m,*** the results demonstrate that the sensor can provide an effective technical means for the measurement of EMP in the time domain.

关键词： EMP sensor antenna

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APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

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Computers, Materials & Continua 2023年第6期75卷 5229-5254页

作者： Meng Wang Yixuan Shao Haipeng Liu Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China

In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing *** to this,this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network *** proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology,thereby the generalization performance and the convergence stability are *** the generator,a Flow-based network using Wavelet additive coupling(WAC)layers is implemented to extract multi-scale content ***,a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input *** enhance the generated salient details,a loss of adaptive stroke edge is applied in both the global and local model *** experimental results show that the proposed method improves PSNR by 5%,SSIM by 6.2%,and decreases Style Error by 29.4%over the existing models on the ChipPhi *** competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization,thereby can be further applied to more APST scenes.

关键词： Artistic painting style transfer reversible network generative adversarial network wavelet transform

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Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

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Frontiers of Computer science 2025年第2期19卷 137-138页

作者： Enchang ZHU Zhengtao YU Yuxin HUANG Shengxiang GAO Yantuan XIAN Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long *** this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational ***,this method first constructs the structural graph by extracting elements from the source document.

关键词： contextual spite semantic

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LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

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Computers, Materials & Continua 2024年第10期81卷 951-969页

作者： Muhammad Naeem Ul Hassan Zhengtao Yu Jian Wang Ying Li Shengxiang Gao Shuwan Yang Cunli Mao Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming650500China

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding ***,the performances of these models drop sharply when the scale of the parallel training corpus is *** the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language *** alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation *** the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic *** the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation *** on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT *** ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

关键词： Urdu NMT(neural machine translation) Urdu natural language processing Urdu Linguistic features low resources language linguistic features pretrain model

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Boundary-Enhanced U2-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4227-4240页

作者： Meng, Yuanqin Chai, Shengjie Xiao, Haoyu Meng, Zhaohui Wang, Qingwang Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China First Affiliated Hospital of Kunming Medical University Department of Cardiology Kunming650032 China

The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables comprehensive observations of the entire heart, especially through the apical 4-chamber view. However, no current clinical indices evaluate cardiac function considering all four chambers simultaneously. Manual estimation of the four chambers is laborious, inefficient, and complicated by anatomical complexity and variable image quality, including motion artifacts and unclear borders. There is a significant need for a high-performance segmentation tool that can assess all four chambers concurrently. To address this, we collected a clinically representative dataset of 2D apical 4-chamber view echocardiograms, with annotated 4-chamber regions serving as the basis for automatic 4-chamber synergy analysis. We then proposed a boundary-enhanced network, denoted as BeU2-Net, tailored for transthoracic echocardiography 4-chamber segmentation using our private dataset. Specifically, our network employs a two-level nested encoder-decoder architecture, utilizing a segmentation-specific residual U-block with a mixture of receptive fields at each stage to capture multi-level and multi-scale features. A dedicated boundary prediction branch, incorporating edge details, is integrated to enhance boundary segmentation performance. Experiments on both private and public datasets demonstrate that our BeU2-Net possesses superior boundary detection capabilities and achieves high segmentation performance for echocardiographic images. The source code and dataset will be released at https://***/mm666-y/BeU2-Net. © 2013 IEEE.

关键词： Heart

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Research on Citrus Leaf Disease Recognition Using Class-Agnostic Contrastive Learning and Supervised Organizational Mapping

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IEEE Access 2025年 13卷 101592-101608页

作者： Xiao, Wen Shang, Jinxia Li, Fan Ao, Ou Wang, Xin Tian, Shiyi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Lab of Computer Technology Applications Kunming650500 China

Early detection of plant diseases is crucial for enhancing agricultural productivity and ensuring crop protection. While computer vision offers scalable alternatives to manual inspection, existing methods face two understudied challenges: (1) performance degradation in cluttered real-world conditions, and (2) ineffective feature separation for randomly distributed lesions with diverse symptoms. Notably, current research predominantly relies on controlled-environment datasets, lacking publicly accessible benchmarks with authentic field complexities. To address these, we present the first large-scale citrus leaf disease dataset (5,763 images, 6 categories) capturing complex field conditions including varying illumination, occlusion, multi-scale lesions, and deceptive backgrounds. Our key methodological innovations include a three-stage weakly supervised framework that systematically resolves background interference and feature redundancy: (i) Class-agnostic contrastive localization enabling precise lesion mapping without region annotations, (ii) Stable feature initialization through region-guided classification training, (iii) Sparse orthogonal mapping with experimentally-determined nodes introducing orthogonality constraints to enhance feature separation. Through feature alignment and feature fusion, an accuracy of 97.1% is achieved (+2.4% vs ResNet-18). Evaluations on citrus leaf and public datasets demonstrate superior accuracy and interpretable lesion localization, advancing plant disease diagnosis. © 2013 IEEE.

关键词： Citrus fruits

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