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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Yu, Hua Zheng, Kaiwen Li, Hui Liang, Fengna Zhang, Jinxin Zhang, Fuquan Ocean College Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811650352

Forest vegetation is an important place for energy conversion and material cycle, and leaf area index (LAI) is an important parameter to describe the process of plant canopy function. In this paper, with the support of computer technology such as VC, the LAI of Fujian Province from 2001 to 2017 was analyzed and studied by using MODIS remote sensing data with 500 m resolution every 8 days. The results showed that the vegetation growth was the most vigorous in the 200–800 m terrain. The evergreen coniferous forest was mainly distributed in the northwest of Fujian Province, while the mixed forest in the interior. The seasonal mean value of LAI for many years reaching 3.5was the highest in the summer and autumn;the second highest was in the spring, with the mean value of LAI reaching 2.0;the lowest was in the winter, with the mean value of LAI reaching 1.5. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conversion

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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Ren, Hui Zhang, Chengya Sun, Ke Ye, Chenyu Communication University of China School of Information and Communication Engineering Beijing100024 China Ministry of Culture and Tourism Key Laboratory of Audiovisual Technology and Intelligent Control System Beijing100024 China Beijing Key Laboratory of Modern Performing Arts Technology Beijing100024 China University of Pennsylvania School of Engineering and Applied Science PA19104 United States

ISBN: (纸本)9798350380347

Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method. © 2024 IEEE.

关键词： Generative adversarial networks

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Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Skewed Pins Detection of Aviation Connectors Based on Multi-...

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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Jiaming Li Cheng Ren Jiaxin Xu Yao Wang Hanlin Xu Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection.

关键词： Connectors Point cloud compression Wiring Three-dimensional displays Accuracy Manuals Inspection Pins Artificial intelligence Defect detection

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Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding

arXiv

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arXiv 2024年

作者： Dong, Cunhui Ma, Haichuan Zhang, Haotian Gao, Changsheng Li, Li Liu, Dong The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework – H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework – iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding. Copyright © 2024, The Authors. All rights reserved.

关键词： Image coding

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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2021 China Automation Congress, CAC 2021

作者： Kang, Lu Liu, Ding Wu, Yali Ping, Guozheng School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781665426473

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm. © 2021 IEEE

关键词： Genetic algorithms

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GOGICP: A Real-time Gaussian Octree-based GICP Method for Faster Point Cloud Registration

GOGICP: A Real-time Gaussian Octree-based GICP Method for Fa...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yu, Zhitao Yuan, Wei Zhao, Hengwang Zhuang, Hanyang Yang, Ming Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Jiao Tong University Global Institute of Future Technology Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9798350372601

Point cloud registration algorithms are crucial for robot localization based on prior maps. Classical methods often struggle to achieve both high accuracy and real-time performance simultaneously, as these two goals can conflict. This limitation impedes the application of point cloud registration in high-speed scenarios, such as the LiDAR localization of fast-moving robots, where both precision and real-time capabilities are essential. This paper proposes a real-time Gaussian Octree-based Generalized Iterative Closest Point (GICP) method, which leverages the censoring properties of the Gaussian Octree to expedite the nearest neighbor search and incorporates an incremental calculation of the Gaussian distribution to hasten the tree-building process. Experimental results show that the proposed method can achieve a 61.8% improvement in speed over GICP while maintaining accuracy comparable to that of GICP. The average registration accuracy is up to 5 cm, with an angular error of less than 0.2°, and the average registration time for a single frame is 77 ms, which satisfies the demands of practical application scenarios. © 2024 IEEE.

关键词： Gaussian distribution

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

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Annual Conference of Industrial Electronics Society

作者： Yu Wu Bo Yang Dafeng Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy.

关键词： Vibrations Industrial electronics Accuracy Computational modeling Mechanism design Predictive models Logic gates Real-time systems Data communication Industrial Internet of Things

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th International Conference on information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 IEEE.

关键词： Image matching

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