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Multispectral point cloud superpoint segmentation

Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Multi-Modal Instrument Recognition Method Based on Improved YOLOv5s and ESPNet 6

Multi-Modal Instrument Recognition Method Based on Improved ...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Shen, Shixian Wang, Jianguo Feng, Yong Li, Yingna Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China China Copper Industry Co. Ltd Kunming China

ISBN: (纸本)9798350374315

Addressing the challenges of high cost, low accuracy, and poor real-time performance in anomaly detection within industrial production processes, this study proposes a multi-modal instrument recognition method based on improved YOLOv5s and ESPNet. Specifically, a dynamic non-monotonic focusing mechanism is introduced into the YOLOv5s object detection model, enhancing its ability to accurately detect the position of pointer-type instruments. Simultaneously, affine and perspective transformations are employed to rectify skewed and rotated images. Secondly, variable convolutions are integrated into the ESPNet segmentation network to adaptively capture nonlinear deformation elements of pointers and dials in images, thereby extracting comprehensive key information from both. Subsequently, a Hough transform operation is applied to fit the straight line representing the pointer, while contour tracking methods are utilized to extract the valid range of dial data. Based on linear proportional relationships, the reading of pointer-type instruments is computed. Finally, a comprehensive model is built by integrating image-acquired instrument data with corresponding sensor data, enabling anomaly detection from a multimodal data analysis perspective. Experimental results demonstrate an identification accuracy of 96.21% for pointer-type instruments, with an average detection speed of 0.216 seconds and a data anomaly detection rate of 99.43%. This method accurately and rapidly identifies data anomalies, meeting the demands for key indicator monitoring in industrial production. © 2024 IEEE.

关键词： Hough transforms

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Visible and Infrared Image Fusion for Object Detection: A Survey

Visible and Infrared Image Fusion for Object Detection: A Su...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Sun, Yuxuan Meng, Yuanqin Wang, Qingbo Tang, Minghua Shen, Tao Wang, Qingwang School of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Naval Research Academy Beijing China

ISBN: (纸本)9789819708543

Thanks to the advancements of Deep Learning (DL) Algorithms. DL-based object detection models has witnessed remarkable success in the past few years. By leveraging deep convolutional neural networks (CNNs) and other deep learning models, rich feature representations can be effectively extracted from visible (RGB) images for object detection. However, for challenging scenarios such as low-light conditions and haze, the performance of object detection using visible images is often not satisfactory. On the other hand, thermal camera, which is unaffected by lighting conditions, can penetrate through low-light and hazy environments to capture object images. However, infrared images lack edge and texture information of objects. Recognizing the complementary nature of visible and infrared images, researchers have explored the fusion of visible and infrared images for object detection, yielding promising research outcomes. This paper provides an analysis of the current research status of visible and infrared image fusion for object detection. Firstly, the fusion models are categorized into three kinds: pixel-level fusion, feature-level fusion, and decision-level fusion. Several models within each category are discussed. Furthermore, this paper summarizes five datasets that can be utilized for training RGB-Infrared object detection models and compares the experimental results of selected models on the KAIST dataset. Lastly, the paper concludes with a summary of existing challenges in the field and offers some reflections on future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Developing microwave-induced thermoacoustic tomography:System,application,and reconstruction

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Journal of Electronic Science and Technology 2023年第4期21卷 1-19页

作者： Shuang-Li Liu Wan-Ting Peng Xiao-Zhang Zhu Jian Song Xin Shang Hua Zhang Zhi-Qin Zhao School of Information Engineering Southwest University of Science and TechnologyMianyang621010China School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China

A microwave-induced thermoacoustic imaging(MITAT)system is a non-destructive physical medical imaging method that combines the advantages of the high contrast of microwave imaging and the high resolution of ultrasound *** uses the microwave as the excitation source and ultrasound as the information *** different kinds of biological tissue absorb electromagnetic energy,it results in localized temperature *** thermal expansion will induce ultrasonic signals(i.e.,thermoacoustic signals),known as the thermoacoustic *** microwave absorption image of the sample can be reconstructed by algorithm *** MITAT contrast depends on different dielectric parameters of different kinds of *** introduce the developed system and its *** addition,the challenges and prospects of MITAT for further development are discussed.

关键词： application Microwave-induced thermoacoustic tomography(MITAT) Reconstruction System

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Dynamic performance and energy efficiency of reflective and insulative composite coating on building exterior wall

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Building Simulation 2023年第12期16卷 2245-2259页

作者： Guangpeng Zhang Huijun Wu Jia Liu Jianming Yang Huakun Huang Yujie Ding Lei Xie School of Civil Engineering Guangzhou UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies Guangzhou 510006China School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China

Reflective and insulative composite coatings are a new energy-saving material with high solar reflectance and extremely low thermal conductivity for *** optimization and impact of high solar reflectance and low thermal conductivity on the insulating capacity of walls remain *** work investigates the dynamic thermal performance and energy efficiency of a reflective and insulative composite coating in regions with hot summer and warm winter.A simplified thermal resistance-heat capacitance model of an exterior building wall is established to predict thermal *** dynamic temperature and heat flow of the wall are predicted to reduce heat loss through the interior surface of the wall and compared to the conventional *** specific impact of the thermal conductivity and solar reflectance of the coating on the heat loss is further investigated to minimize heat loss of the *** research shows that the composite coating shows better performance on adjusting outdoor climate change than the other *** with cement,it reduces the maximum temperature of the exterior surface of the wall by 7.45°C,and the heat loss through the interior surface of the wall by 38%.The heat loss is reduced with the increase of solar reflectance and the reduction of thermal *** results can provide a useful reference and guidance for the application of reflective and insulative composite coating on building exterior wall to promote their energy-saving use on building envelopes.

关键词： reflective and insulative composite coating exterior wall building insulation solar reflectance thermal conductivity

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition 11

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Liu, Yingli Zhang, Guangtao Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table's structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table's structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology. © 2023 IEEE.

关键词： Forecasting

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset 11

Multi-objective performance prediction based on a self-const...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Hu, Dan Liu, Yingli Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. © 2023 IEEE.

关键词： Feature Selection

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Existence and Non-Existence of Solutions to Some Degenerate Coercivity Quasilinear Elliptic Equations with Measure Data

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Journal of Mathematical Research with applications 2022年第2期42卷 173-188页

作者： Maoji RI Xiangrui LI Qiaoyu TIAN Shuibo HUANG School of Mathematics and Computer Science Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University

In this article,we study the existence and non-existence of weak solutions to the following quasilinear elliptic problem with principal part having degenerate coercivity and nonlinear term involving gradie,■where??RN... 详细信息

关键词： elliptic equation degenerate coercivity measures data existence non-existence

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