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11th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2022

作者： Wang, Hairui Li, Yuanbo Zhang, Wenqi Duan, Yusu Zhu, Guifu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Information Technology Construction Management Center Kunming University of Science and Technology Kunming China

ISBN: (纸本)9783031288159

At present, the large-scale railway maintenance equipment adopts a diesel engine as the main power plant. Therefore the diesel engine in the event of failure, will seriously affect the large-scale railway maintenance equipment of the normal work. Exploring advanced diesel engine condition monitoring and fault diagnosis technology and looking for practical and effective diesel engine fault diagnosis method, which has already become a research subject widely concerned by many experts at home and abroad. In this paper, genetic algorithm (GA) is used to optimize the parameters of radial basis function (RBF) neural network for diesel engine fault diagnosis, experimental results show the validity of this prediction method, and the accuracy of the proposed algorithm was verified by comparative. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Failure analysis

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BAMGAN: A KBQA method based GAN and bi-directional attention 6

BAMGAN: A KBQA method based GAN and bi-directional attention

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6th International Conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Guo, Jiazhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650504 China

ISBN: (纸本)9781510668249

Knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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Multiframe Blind Deconvolution based on Bright and Dark Channel Prior

Multiframe Blind Deconvolution based on Bright and Dark Chan...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Ai, Haotian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650000 China

ISBN: (纸本)9781510671881

Blind image restoration is a challenging and valuable subject in the field of computer vision. In this paper, under the framework of Total Variation (TV) regularization term, the bright and dark channel regularization term is introduced into the multi-frame image blind restoration algorithm, aiming at overcoming the shortcomings of low contrast and obvious ringing effect of the restored image on the basis of retaining the advantages of TV regularization. The bright channel regularization term can estimate the blur degree of the degraded image more accurately, while the dark channel regularization term can better resist noise interference. The split Bregman iterative algorithm is used to solve the objective function, and the details of the restored image are compensated in the iterative process, and further. The experimental results show that the algorithm in this paper has a certain deblurring effect, and the peak signal-to-noise ratio of the restored image is improved by about 0.53dB~1.5dB, and the structural similarity is improved by about 0.12~0.47, with better visual effects. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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Stability and Thermal Property Optimization of Propylene Glycol-Based MWCNT Nanofluids

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Fluid Dynamics & Materials Processing 2023年第9期19卷 2399-2416页

作者： Xi Wang Shan Qing Zhumei Luo Yiqin Liu Zichang Shi Jiachen Li Faculty of Metallurgical and Energy Engineering Kunming University of Science and TechnologyKunming650093China Faculty of Media and Information Engineering Yunnan Open UniversityKunming650093China

Propylene glycol-based MWCNT(multi-walled carbon nanotubes)nanoﬂuids were prepared in the framework of a two-step method and by using a suitable PVP(polyvinyl pyrrolidone)*** BBD(Box-Behnken design)model was exploited to analyze 17 sets of experiments and examine the sensitivity of the absorbance to three parameters,namely the concentration of MWCNT,the SN ratio(mass ratio of carbon nanotubes to sur-factants)and the sonication *** results have revealed that,while the SN ratio and concentration of MWCNT have a strong effect on the absorbance,the inﬂuence of the sonication time is less *** sta-tistical method of analysis of variance(ANOVA)was further used to determine the F-and p-values of the *** experiments were run to validate this *** sample 2 was found to display the greatest absorbance,it was selected for stability monitoring as well as thermal conductivity and viscosity *** sample has been found to be stable;the viscosity decreased with increasing temperature;the addition of MWCNT nano-particles was more effective in improving the thermal conductivity of propylene glycol than other methods in the ***,the MWCNT nanoﬂuid based on propylene glycol exhibited higher thermal conductivity at low temperatures.

关键词： Propylene glycol thermal conductivity response surface methodology stability viscosity

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Analysis of the Correlation between Serum 25 Hydroxyvitamin D and Blood Lipids in Middle-Aged and Elderly People in Anning District, kunming City 23

Analysis of the Correlation between Serum 25 Hydroxyvitamin ...

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15th International Conference on Bioinformatics and Biomedical technology, ICBBT 2023

作者： Sun, Zhiran Song, Chao Zhang, Hongjiang Lu, Chengxin Qin, Zhengyu Guo, Ling Xiong, Xin He, Jianfeng Faculty of Information Engineering and Automation Kunming University of Science and Technology China China

ISBN: (纸本)9798400700385

Dyslipidemia is a metabolic disorder that can increase the risk of cardiovascular diseases. This study aims to investigate the association between serum 25- hydroxyvitamin D(25(OH)D) levels and blood lipid abnormalities in middle-aged and elderly residents of Anning District, kunming City. It provides a theoretical basis for the prevention and treatment of cardiovascular diseases in the region. A total of 6,391 middle-aged and elderly individuals from the First People's Hospital of Anning City, Yunnan Province, were categorized based on serum 25(OH)D levels. Pearson correlation analysis, multiple linear regression, and other methods were used to examine the relationship between 25(OH)D and blood lipid abnormalities. The results showed that the Body Mass Index(BMI), Total Cholesterol(TC), and Triglyceride (TG) levels in the severely deficient group, deficient group, and insufficient group were significantly higher than those in normal group (P © 2023 ACM.

关键词： Diseases

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An Improved Multi-Site Feature Detection Model for Knee Arthritis Based on YOLOv5s 3rd

An Improved Multi-Site Feature Detection Model for Knee Arth...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Liu, Yingli Huang, Yishan Cha, Yinqiu Gao, Ming Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China The First People’s Hospital of Yunnan Province The Affiliated Hospital of Kunming University of Science and Technology Yunnan Kunming China

ISBN: (纸本)9789819624676

Addressing the needs and challenges of multi-site feature detection for knee arthritis, this paper proposes an improved lightweight network model, MMG_YOLOv5s. This model aims to solve the problems of complex structures and massive parameter counts in traditional detection models, which hinder their deployment on edge devices. The M(1) and M(2) modules in the MMG_YOLOv5s model enhance the YOLOv5s network by adjusting its backbone and neck structures, decreasing the count of network parameters and compressing the model's size. This, in turn, improves computational efficiency and creates conditions for deployment on resource-constrained devices. The data for this experiment is sourced from the public dataset OAI and annotated under the guidance of professional doctors to construct our own dataset. The experimental outcomes indicate that the recognition accuracy of this model achieves 80.8%, representing an 11% enhancement in comparison to the original YOLOv5s model. More importantly, its parameter count is reduced by 71.27%. This improvement provides a solid foundation for the subsequent embedded deployment of the model into portable or mobile devices, promising to advance the intelligent and convenient process of knee arthritis detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Design of high-transmittance anomalous refraction metasurface based on geometric phase 9

Design of high-transmittance anomalous refraction metasurfac...

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9th International Conference on Energy Materials and Electrical engineering, ICEMEE 2023

作者： Zhang, Li Sun, Jun Hu, Mingzhi Chang, Kairong Dai, Shuhao School of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650000 China

ISBN: (纸本)9781510672765

In this paper, a transmission metasurface based on a geometric phase is proposed. By designing and arranging the metasurface unit structure, the wavefront of the incident circularly polarized wave can be manipulated arbitrarily. The metasurface unit is composed of a typical "sandwich"structure, metal-dielectric-metal, and the top and bottom metal patterns are composed of C-shaped and rectangles. Using the geometric phase principle, the phase can be adjusted by rotating the metal structure at the operating frequency, and the cross-polarization transmission amplitude is as high as 90 %. By rotating the coding of the unit structure, a coding metasurface can be formed, resulting in abnormal deflection. The simulation results show that the designed coding metasurface can regulate the electromagnetic wave well at 0.63 THz, and the abnormal refraction angles are ± 50.1° and 30°, which well verifies the generalized Snell's law and has potential application value in the fields of lidar and so on. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Refraction Terahertz radiation Design and modelling Wavefronts Structural design

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Cross-Guided Attention and Combined Loss for Optimizing Gastrointestinal Visual Question Answering

Cross-Guided Attention and Combined Loss for Optimizing Gast...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Qian Liu, Lijun Yang, Xiaobing Liu, Li Peng, Wei Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming650500 China

ISBN: (纸本)9798350386226

Medical Visual Question Answering (Med-VQA) aims at answering clinical questions related to medical imaging, providing important reference for medical imaging diagnosis. Gastrointestinal visual question answering can deeply understand and analyze gastrointestinal medical images, provides supporting information to answer questions about the diagnostic process, and improves diagnostic accuracy and efficiency. However, most existing Med-VQA can only handle simple and general questions, struggling to tackle visual question answering related to complex diseases. Therefore, this paper proposes a Med-VQA model called CACL, which uses cross-guidance and improved combined loss. The model focuses on gastrointestinal image analysis and achieves efficient and accurate feature extraction for gastrointestinal images. A cross-guided attention module is also designed to enhance the model's reasoning ability when dealing with complex cross-modal tasks. In addition, a multi-task composite loss function is designed to balance the loss of segmentation task and classification task, and improve the overall performance. Experimental results show that the proposed method can effectively improve the accuracy of gastrointestinal visual question answering. © 2024 IEEE.

关键词： Multi-task learning

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A fine segmentation model of flue-cured tobacco’s main veins based on multi-level-scale features of hybrid fusion

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Soft Computing 2024年第17-18期28卷 10537-10555页

作者： Xu, Biao Liu, Xiaobao Gu, Wenjuan Liu, Jia Wang, Hongcheng Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China School of Electronic Information Engineering China West Normal University Nanchong637000 China

Flue-cured tobacco (FCT) can be classified into upper (B), middle (C), and lower (X) parts based on characteristics such as the FCT's main veins, leaf shape, color, and thickness. Accurately measuring the geometric parameters of the main veins is crucial for identifying the different parts. However, this task has proven to be challenging. Therefore, segmenting the main veins is a prerequisite to reducing calculation errors and improving the precision of part identification. To obtain enough semantic information and improve segmentation accuracy, we propose a fine segmentation model (MSHF-Net) of FCT's main veins based on multi-level-scale features of hybrid fusion. Firstly, MobileNetV2 with a dilated convolution layer (DMobileNetV2) is selected as the backbone network for feature extraction, which optimizes training and inference speed to minimize computing costs. Subsequently, Hybrid Fusion Atrous Spatial Pyramid Pooling (HFASPP) is designed to be the strengthened backbone module for capturing more high-level semantic information, effectively preventing intermittent segmentation of some main veins. Additionally, considering the low proportion of main vein targets in the original image, the double shallow feature branches (DSFBS) are included to obtain more low-level semantic information. Finally, a channel attention mechanism (ECANet) is added to enhance useful information and eliminate redundant information after the hybrid fusion of high-low-level semantic information, preventing mis-segmentation of regions. Experimental validation demonstrates the efficiency of the MSHF-Net, with parameters of only 7.92 M, thus ensuring minimal computational requirements. The model achieves an impressive mean intersection over union (MIoU) of 85.57% and mean pixel accuracy (mPA) of 93.10% on a diverse test set of FCT parts. When applied to segment main veins in a 2296 × 1548 × 3 tobacco image, the model takes just over 0.1 s. It is noteworthy that none of the 291 randomly segmen

关键词： Semantic Segmentation

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