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Research on Double Attention Mechanism High-resolution Network for Human Pose Estimation

Engineering Letters 2025年第2期33卷 338-347页

作者： Li, Haisheng Yuan, Rongrong Chen, Jingyin Li, Qiuyi Hu, Cong Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guangxi Guilin541004 China School of Electronic and Information Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guangxi Guilin541004 China

Recently, high-resolution networks combining different resolution features have achieved good results in human pose estimation tasks. However, there are still some problems for high-resolution networks, such as insufficient use of important feature information and lost information in the sampling process. This paper proposes a double attention mechanism high-resolution network based on human pose estimation for these problems. The double attention mechanism consists of coordinate and downsampling attention modules. The coordinate attention module can enhance the feature representation of the network by extracting more useful node information. The downsampling attention module reuses the initial feature information to predict the occluded parts better and accurately estimate the corresponding point positions of human joints. Experimental results on MPII and COCO datasets show that the proposed network model effectively improves the accuracy of keypoint estimation in human pose estimation tasks. Especially, our network model achieves 78.1% average precision (AP) on the COCO dataset. © 2025, International Association of Engineers. All rights reserved.

关键词： Importance sampling

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Application of the CNN-GRU-MHA Model in Partial Discharge Recognition 24

Application of the CNN-GRU-MHA Model in Partial Discharge Re...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Bi, Jinjie Chen, Guofu Li, Tinghui Huang, Shoulin Shen, Biquan Yang, Rixing Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Guangxi Normal University Guilin China

ISBN: (纸本)9798400712883

Partial discharge (PD) is one of the most important factors affecting the stable operation of high-voltage equipment and poses a great threat to the reliability and safety of power systems. Although some deep learning-based methods, such as Convolutional Neural Networks (CNN), have been proposed for PD detection, there are still some shortcomings in terms of global dependencies. In this paper, it is proposed to develop a model of Convolutional Neural Networks-Gated Recurrent Unit-Multi-Head Attention (CNN-GRU-MHA). Wavelet transformation is adopted for noise suppression. reduction, together with statistical feature downsampling for reducing data dimensionality. In the model, CNN extract localized temporal features, GRU capture sequential temporal patterns, and MHA focuses on modeling global dependencies while highlighting critical features. Experimental evaluations reveal that the model reaches a Matthews correlation coefficient of 74.78% and a recall rate of 74.61%, performing better than some conventional methods in terms of both accuracy and robustness. This model securely and effectively provided a method of enhancing power system safety and stability. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Lesion Segmentation Network Based on U-shaped Network 8

A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Le...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Wu, Rongqing Song, Haixing Zeng, Xuqing Li, Guanglian Zhang, Junyu He, Fuyun Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Key Laboratory of Integrated Circuits and Microsystems Guangxi Normal University Education Department of Guangxi Zhuang Autonomous Region Guilin China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

With the continuous development of deep learning, computer-aided diagnosis reduces the pressure of dermatologists. Although many U-Net-based convolution neural networks can achieve effective segmentation, they still need to be improved when faced with challenging skin lesion segmentation tasks such as fuzzy and irregular lesions. To this end, we propose a skin lesion segmentation algorithm (MSMF-Net) based on U-shaped network architecture, which mainly includes the following aspects: the feature distribution of different encoding stages are fused and fully utilised by the EFM module. To better handle high-level semantic features, multiscale and attention parallel module (MS-AM) is introduced at the bottleneck of the network. Attention bridging module (ABM) is used at skip connection, which enables effective local and global information fusion and provides rich feature information for decoding. The decoding fusion module (DFM) processes the decoded information by flexibly embedding channel and spatial attention into the fusion process, so that the information is more concentrated in the lesion area. Our network is compared with other state-of-the-art methods on two public datasets and obtains the best results. © 2025 SPIE.

关键词： Convolutional neural networks

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Novel Dictionary Pair Learning through Locality-Constrained Profiles for the Identification of Chili Pepper Varieties via E-noses

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IEEE Sensors Journal 2025年第10期25卷 17530-17540页

作者： Chen, Liang Peng, Guihua Zhang, Changfu Wang, Xueya Chen, Ju Yin, Yong Yan, Jia Southwest University College of Artificial Intelligence Chongqing400715 China Guizhou Academy of Agricultural Sciences Chili Pepper Research Institute Guiyang550006 China Southwest University College of Artificial Intelligence Beibei Chongqing400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China

In modern society, to make it easier for customers to assess product quality and regularize distribution and supply, almost all crops are sorted and labeled. This sorting and labelling process also facilitates packaging and transportation of products and benefits farmers. As an emerging technology, electronic noses (E-noses) can be applied to food quality control. In this study, we proposed a novel dictionary pair learning model named dictionary pair learning based on locality constraint of profiles (DPL-LCP), which achieved better performance in the identification of chili pepper varieties using E-noses. This model incorporates profile vectors to define a profile locality constraint term and integrates it with a dictionary reconstruction error term into a unified dictionary learning model to fully learn the characteristics of samples from various aspects. This approach not only reduces the similarity between dictionary atoms but also avoids the problem of weakening the connections between characteristics of different classes of samples caused by using the block diagonal constraint. In addition, the model utilizes the l2,1 norm to regularize the analysis dictionary matrix, enhancing the sparsity of features and thereby enhancing the flexibility and robustness of the model. The experimental results show that the proposed model can achieve better classification results and higher stability than other advanced dictionary learning models can. With this method, it is possible to reliably identify 13 varieties of chili peppers based on flavor assessment. © 2025 IEEE.

关键词： Contrastive Learning

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A multi-strategy enhanced dung beetle algorithm for solving real-world engineering problems

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Artificial Intelligence Review 2025年第8期58卷 1-79页

作者： Mao, Zhengxing Yang, Zhen Luo, Dan Lin, Dong Jiang, Qinghong Huang, Guoxian Liao, Zhixian College of Electronic and Information Engineering Guangxi Normal University Guilin China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University) Education Department of Guangxi Zhuang Autonomous Region Guilin China Guilin University Guilin China

The Dung Beetle optimization (DBO) algorithm is an innovative and effective metaheuristic algorithm widely recognised for its excellent numerical optimization performance. However, DBO converges slowly and tends to fall into local optima due to the imbalance between exploration and exploitation, the lack of collaborative search capability and population diversity. To overcome these challenges, this paper proposes a dung beetle optimization algorithm based on multi-strategy collaborative enhancement (MDBO). The algorithm constructs a “search-enhance-escape” collaborative optimization framework through adaptive regulation and elite information sharing, and contains three major innovations: (1) a dual adaptive search strategy, which combines the adaptive contraction mechanism of the leader’s centre of mass and the brownian bidirectional crossover perturbation strength regulation, to enhance the population diversity and collaborative search ability of the dung beetle, achieving a dynamic balance between exploration and exploitation; (2) Elite Enhanced Solution Quality (EESQ) mechanism, which improves the quality of both the current local and global optimal positions and accelerates convergence through structured elite information and dual-phase adaptive perturbation; and (3) Dynamic Oppositional Learning (DOL), which introduces an asymmetric adaptive perturbation in the dung beetle’s foraging and Breeding phases and enhances the ability to escape from local optima. The three act synergistically to achieve a more efficient optimised search. The performance of MDBO is evaluated using the IEEE CEC 2017, CEC 2019 and CEC 2020 benchmarking functions. Compared to the DBO algorithm, the MDBO algorithm improves the convergence accuracy and stability on the CEC2017 benchmark functions by 60.91 % and 63.98 %, respectively. For the CEC2019 benchmark functions, the corresponding improvements are 54.47 % and 41.36 %, while for the CEC2020 benchmark functions, they are 50.71 % and 55

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Temsa:Text Enhanced Modal Representation Learning for Multimodal Sentiment Analysis

SSRN

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SSRN 2025年

作者： Chen, Jingwen Song, Shuxiang Tan, Yumei Xia, Haiying The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Guilin China

Multimodal sentiment analysis aims to identify human emotions by leveraging multimodal information, including language, visual, and audio data. Different modalities contribute unequally to sentiment analysis, with text being more effective than non-linguistic modalities in multimodal sentiment analysis (MSA) due to its intuitive emotional expressiveness. A major challenge lies in leveraging the strengths of text to create fused representations that capture rich emotional content while preserving both inter-modal and intra-modal relationships. To address this challenge, we propose a Text-Enhanced Modal Representation Learning Model (TEMSA), which builds robust and unified multimodal representations through the design of text-guided pairwise cross-modal mapping modules. Specifically, TEMSA employs a text-guided multi-head cross-attention mechanism to embed linguistic information into the emotion-related representation learning of non-linguistic modalities, thereby enhancing the representations of visual and audio modalities. In addition to preserving consistent information through cross-modal mapping, TEMSA also incorporates text-guided reconstruction modules, which leverage text-enhanced non-linguistic modal features to decouple modality-specific representations from non-linguistic modalities. This dual representation learning framework captures inter-modal consistent information through cross-modal mapping, and extracts modal difference information through intra-modal decoupling, thus improving the understanding of cross-modal affective associations. The experimental results on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that TEMSA achieves state-of-the-art performance, highlighting the critical role of text-guided cross-modal and intra-modal representation learning in multimodal sentiment analysis. © 2025, The Authors. All rights reserved.

关键词： Linguistics

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Enhancing the Robustness of Random Boolean Networks by Epigenetic Regulation

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 261-270页

作者： Junxiu Liu Jufang Dai Qiang Fu Min Su Yuling Luo Sheng Qin Su Yang Lingxi Ma Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Nonlinear Circuits and Optical Communications Guangxi Normal University Guilin China Department of Computer Science Swansea University Swansea U.K.

Random Boolean Network (RBN) is a type of regulatory network in which the nodes have Boolean values representing their states. The robustness of RBNs against perturbations is a crucial characteristic, and there has been a growing interest in enhancing the network's robustness. In this study, a biologically inspired epigenetic regulation method is proposed to enhance the robustness of the RBNs. A frequency encoding method based on pulse counting is employed to encode the node states within a sliding time window, thereby improving the form of epigenetic regulation. To verify the performance of this method, an antifragility indicator is adopted to measure the robustness of RBNs and yeast cell networks at different scales. The experimental results demonstrate that the networks with epigenetic regulation exhibit excellent robustness, even in the presence of large-scale networks and severe perturbations. This approach provides a new perspective and idea for designing robust RBNs and discrete networks.

关键词： Epigenetics Robustness Regulation Perturbation methods Complexity theory DNA Gene expression Evolution (biology) Encoding Computational biology

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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems

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Journal of King Saud University Computer and Information Sciences 2025年第4期37卷 1-22页

作者： Zhang, Ruoxin An, Angela Cen, Mingcan Zhang, Chong Huangfu, Aimee Vinnakota, Sunny Song, Shuxiang Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University) Education Department of Guangxi Zhuang Autonomous Region Guilin China Faculty of Science Engineering and Built Environment Deakin University Geelong Australia Academies Australasia Polytechnic Melbourne Australia

Monitoring fragmented mangrove ecosystems presents significant challenges due to their sparse distribution and the limitations of traditional detection methods, which often suffer from poor convergence and high rates of false positives and false negatives. To address these issues, we propose FragMangro, a cross-domain zero-shot detection model designed to enhance the accuracy and generalization of mangrove detection. FragMangro integrates parallel multi-scale convolution for progressive dimensional upscaling, improving spectral feature extraction. Additionally, it employs a dynamic learning rate and iterative control algorithm, which adaptively adjusts learning rates and training epochs based on loss monitoring to prevent suboptimal convergence. We conducted extensive experiments in three major mangrove reserves in guangxi, China. The results demonstrate that FragMangro achieves over 98% accuracy, an average F1 Score exceeding 85%, and the Intersection over Union(IoU) surpassing 74%, significantly outperforming conventional methods such as support vector machines (SVM), random forests (RF), and k-nearest neighbors (KNN). Specifically, in the Beilun estuary, FragMangro exhibits a 0.96% improvement in accuracy over RF, a 17.24% higher F1 Score than SVM, and an 18.08% higher IoU than KNN. Furthermore, the model’s adaptive learning rate and dynamic adjustment strategy enhance convergence efficiency and feature representation. FragMangro serves as an effective tool for ecological monitoring and mangrove conservation, with broader implications for environmental protection and sustainable ecosystem management.

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Improving E-Nose Performance: A Novel ELM-Based Dual-Level Joint Domain Adaptation Method for Sensor Drift Data

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Zijian Zhang, Linxia Li, Zhe Duan, Shukai Yan, Jia Southwest University College of Artificial Intelligence Chongqing400715 China Southwest University Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips College of Artificial Intelligence Chongqing400715 China

The theory and technology of electronic nose (E-nose) systems have been vigorously developed, and these systems have achieved success in many practical applications, such as medical diagnosis, food quality inspection, and environmental detection. However, the drift problem of a sensor array affects the industrialization and commercialization of E-nose systems. In this article, a novel ELM-based dual-level joint domain adaptation method (JDAELM) is proposed to effectively suppress drift and address the distribution discrepancy issue. Specifically, the proposed method implements joint domain adaptation (DA) at the feature level and label level. For source domain data without drift, the information of the data could be preserved as much as possible. Considering the inconsistent distribution caused by drift, the marginal and conditional distribution discrepancies are reduced to a minimum at the feature level to achieve domain alignment. To reduce the impact of pseudolabels on the model, we align the label space to achieve DA at the label level. By maximizing the Hilbert-Schmidt independence criterion, the relationship between the feature projection space and label projection space is strengthened in this model. The joint learning model is effectively solved by an efficient alternative optimization strategy. The average accuracy of the proposed method is 88.30% and 87.21% under long-term drift and short-term drift, respectively, and 96.41% on the instrument variation dataset, which is superior to that of other comparison methods in terms of accuracy. This proves that the JDAELM can be well adapted to long-term and short-term drift scenarios, and can effectively compensate for instrument variation drift caused by inherent differences. © 1963-2012 IEEE.

关键词： Adversarial machine learning

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Phosphorus-nitrogen co-doped graphene: Advancements in fast, highly sensitive ammonia sensing at room temperature

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Li, Quanfu Sun, Manli Li, Fuqian Chen, Zhencheng Luo, Xudong School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

High sensitivity and fast response ammonia gas sensors have wide applications in chemical production, food safety, medical diagnosis, and other fields. This work innovatively utilizes phosphorus and nitrogen as co-doping elements to significantly enhance the ammonia sensing performance of graphene. Ammonia sensing tests have shown that phosphorus-nitrogen co-doped graphene (PNG) exhibits excellent ammonia sensing performance at room temperature: high response (up to 4.34% response to 0.5 ppm ammonia, more than 5 times that of graphene), ultra-low detection limit (33.9 ppb), fast response/recovery (response time (Tres) and recovery time (Trec) are 19 s and 36 s, respectively, which are more than 90% shorter than graphene on average). In addition, the PNG ammonia sensor also has excellent selectivity and good long-term stability. The PNG ammonia sensor studied in this paper has great potential in practical applications, especially in the detection of low concentration ammonia in exhaled breath analysis for medical diagnostics. It is expected to provide a reliable and low-cost solution for various applications of graphene ammonia sensors. © 2025

关键词： Ammonia

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