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A Uniform Distribution of Landmarks for Efficient Map Compression 14th

A Uniform Distribution of Landmarks for Efficient Map Compre...

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14th International conference on computer Vision Systems (ICVS)

作者： Bouaziz, Youssef Royer, Eric Bresson, Guillaume Dhome, Michel Univ Clermont Auvergne Inst Pascal Clermont Ferrand France Inst VEDECOM Versailles France

ISBN: (纸本)9783031441363;9783031441370

In this paper, we address the challenge of visual-based localization in dynamic outdoor environments characterized by continuous appearance changes. These changes greatly affect the visual information of the scene, resulting in significant performance degradation in visual localization. The issue arises from the difficulty of mapping data between the current image and the landmarks on the map due to environmental variations. One approach to tackle this problem is continuously adding new landmarks to the map to accommodate diverse environmental conditions. However, this leads to map growth, which in turn incurs high costs and resource demands for localization. To address this, we propose a map management approach based on an extension of the state-of-the-art technique called Summary Maps. Our approach employs a scoring policy that assigns scores to landmarks based on their appearance in multiple localization sessions. Consequently, landmarks observed in multiple sessions are assigned higher scores. We demonstrate the necessity of maintaining landmark diversity throughout map compression to ensure reliable long-term localization. To evaluate our approach, we conducted experiments on a dataset comprising over 100 sequences encompassing various environmental conditions. The obtained results were compared with those of the state-of-the-art approach, showcasing the effectiveness and superiority of our proposed method.

关键词： Visual-Based Navigation computer Vision for Transportation Long-Term SLAM

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Analysis of Electromagnetic Parameters of Laminated Busbar in Pulsed Power Supply Based on Deep-Developed ANSYS Software

Analysis of Electromagnetic Parameters of Laminated Busbar i...

引用

International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xiao, Yao Shen, Wanzeng Yan, Hongbin Zhao, Jiang Gao, Daqing Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China School of Nuclear Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819620418

The laminated busbar is a very critical component of the accelerator power supply equipment. A large number of large-capacity capacitors, high-power switching tubes and other important components are connected in parallel to it. It works in medium to high frequency and high current pulse mode. The performance of the laminated busbar—especially whether the distribution of inductance and resistance is uniform enough—has a direct impact on the reliable and stable operation of the power supply. In actual engineering practice, it is difficult to evaluate and judge the performance of laminated busbars simply and intuitively. It usually needs to be determined through actual testing after the power supply is manufactured. At this time, if it cannot meet the standards, it will have to be reworked. The process of optimizing the design is therefore time-consuming and laborious. This paper tests the use of finite element analysis method to analyze the inductance and resistance of laminated busbars with a specific structure. To achieve this goal, the script function of the existing commonly used commercial software ANSYS was re-developed to complete the script generator, which in disguise realized the multi-parameter batch scanning analysis and calculation functions required for research. On this basis, the inductor and resistance of a laminated busbar used in actual power supplies were analyzed and calculated, and a design method was proposed to make the distribution of the inductor and resistance more uniform by drilling holes in the laminated busbar metal plate, with good results. It also shows that it is feasible to introduce finite element analysis method into power supply design to analyze the performance of some special components, thereby improving the efficiency of design work. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Laminating

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UWB Radar Signal Kick Detection for Tailgate Unlocking Based on Spatio-Temporal Network 2

UWB Radar Signal Kick Detection for Tailgate Unlocking Based...

引用

2nd IEEE International conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Wang, Yuhan Yan, Shuang Fu, Yiling Shangguan, Tianyun Sun, Yuxuan Sun, Lin Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Westlake University School of Engineering Hangzhou China

ISBN: (纸本)9798350307733

Inductive tailgate systems have significantly enhanced user convenience in vehicle operations. However, the performance of current systems can be hindered by environmental factors, leading to reduced recognition accuracy and durability over time. To address these issues, we propose a kick gesture recognition method based on ultra-wideband (UWB) signals, implemented through a spatial-temporal neural network. Our method starts with employing a Time-Range-Doppler method to extract kick gesture sequences that contain temporal and spatial features from preprocessed data. These sequences are then fed into a network containing both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) cells to predict the final results, where the CNN section is used to learn spatial features and the RNN section is used to learn temporal features. To verify the effectiveness of our approach, we conducted experiments in three typical parking environments: underground garages, overgrown lawns, and sloped terrains, collecting data from 12 participants for model training and testing. Experimental results demonstrated the feasibility of using UWB signals for wireless kick gesture recognition and confirmed the high accuracy of our proposed method. © 2024 IEEE.

关键词： Gesture recognition

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Semi-Mamba: Improving Medical Image Segmentation via Semi-Automatic Mamba Network 41st

Semi-Mamba: Improving Medical Image Segmentation via Semi-Au...

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41st computer Graphics International conference, CGI 2024

作者： Bi, Lei Peng, Yige Feng, David Dagan Kim, Jinman Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science University of Sydney CamperdownNSW Australia

ISBN: (纸本)9783031820236

Segmentation of regions of interest (ROIs) from medical images is considered as a fundamental requirement for many imaging-based clinical decision support systems. Deep learning based automatic segmentation methods have emerged as the state-of-the-art. However, their performance is heavily reliant on using a large number of annotated training dataset to encompass all the possible variations of ROIs, and when the coverage of these variations is inadequate, these methods have difficulties in segmenting the ROIs. Semi-automatic segmentation methods, which fuse user-inputs with high-level semantic image features derived from convolutional neural networks (CNNs) or vision transformers (ViTs) offer an alternative to overcome the limitations of automatic segmentation methods. Unfortunately, CNNs are restricted by the limited receptive fields that cannot capture long-range dependencies, while ViT usually rely on the self-attention mechanism to capture the global context and therefore have high computational complexity. In this study, we propose a Semi-Mamba method for semi-automatic medical image segmentation. The novelty we introduce is to leverage the capability of Mamba to efficiently capture long-range dependency and combine this together with user-clicks to segment challenging ROIs, e.g., ROIs with fuzzy boundaries and inhomogeneous textures. Our experiments with three well benchmarked medical image datasets across different modalities showed that our method consistently outperformed existing automatic and semi-automatic segmentation, which demonstrates strong generalizability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Efficient Enhanced Feature Learning for Remote Sensor Image Object Detection

Efficient Enhanced Feature Learning for Remote Sensor Image ...

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

作者： Chen, Dazhi Gou, Gang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9789819708543

The realm of computer vision has witnessed a profound interest from the scholarly community in the task of remote sensing object detection. Within this article, we present a pioneering and refined approach to feature learning, wherein we harness the potential of the backbone and neck network. We have devised a sophisticated threshold network for the backbone network, facilitating the extraction of profound insights and bolstering our capacity to acquire features by effectively addressing the challenge of high-noise perturbations present in remote sensing imagery. We propose an upgraded feature network paradigm for the neck network, primarily consisting of the Adaptive Feature Enhanced Module (AFEM) and the Gated Convolution Module (GCM). The AFEM is designed to capture contextual information at larger scales, extending the receptive field and preventing feature resolution degradation. On the other hand, the GCM utilizes gated convolution with high-order spatial interactions to adaptively reduce superfluous semantic information. We have undertaken comprehensive experiments on remote sensing image datasets, including DOTA and HRSC, showcasing the exceptional performance of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Feed speed fluctuation in simultaneous five-axis machining using an end mill 15

Feed speed fluctuation in simultaneous five-axis machining u...

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15th International conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool performance, LAMDAMAP 2023

作者： Ihara, Y. Yoshihara, F. Osaka Institute of Technology Japan

ISBN: (纸本)9781998999125

Machining of free-form surfaces using a five-axis machining centre using end mill is attracting attention. Sudden load fluctuations are not desirable in end milling as this causes scratches or dent on the surface. However, although commercially available high-end CAM (computer Aided Manufacturing) software controls the load by changing the depth of cut, the current situation is that CAM software does not fully care in how the generated CL (Cutter Location) data and NC (Numerical Control) programs are processed by the NC unit. When surface scratches occur in actual machining, it is extremely difficult to determine whether the problem is in the tool geometry, the geometric accuracy of the machine, or the performance of the CAM software. Unless the cause is identified, it is difficult to make improvements, which is why we hesitate to introduce 5-axis simultaneous machining. The S-shaped machining test, which was proposed as a performance test for five-axis machining centres, was registered as an ISO standard despite having various problems in each stage of 3D (three dimensional) model generation, machining, and measurement. This test method is more like a CAM software performance test involving software and machine operators than a machine performance test. In this study, a new problem is reported having been discovered by conducting S-shaped machining tests using commercially available CAM software and actual machines. This is a problem that occurs not only in the S-shaped machining test but also in 5-axis simultaneous machining using all end mills. Proposals will be made on how to correct the NC program on site and how to improve the performance of NC units and CAM software. © euspen Headquarters March 2023.

关键词： computer aided manufacturing

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ConeSSD: A Novel Policy to Optimize the performance of HDFS Heterogeneous Storage 24

ConeSSD: A Novel Policy to Optimize the Performance of HDFS ...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Xiao Wang, Liang Huang, Zhijie Xie, Huiru Zhang, Yuchen Ngulube, Michael School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350319934

HDFS(Hadoop distributed file system) is the core storage service of Hadoop, which stores and processes large datasets efficiently. Therefore, the performance of storage services significantly impacts big data processing applications. Using SSD to replace HDD devices can obtain better performance, but due to the high unit price, it is too expensive to replace all HDD with SSD in HDFS. Therefore, heterogeneous storage has been widely used in HDFS. However, the existing hierarchical strategy of HDFS is simple and cannot obtain better cost performance. This paper presents a novel heterogeneous storage policy named Cone_SSD, which fully uses SSD media on each node to get the IO performance to near SSD. Experimental results show that the proposed policy improves read and write performance by 48.11 % and 47.45%, respectively, and the fluctuation of reading performance decreases by 54.92 % compared with other policies under the same price-performance ratio. © 2022 IEEE.

关键词： Large dataset

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performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems 12th

Performance Evaluation of Multi-objective Evolutionary Algor...

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12th International conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test problems are proposed. One question is whether they also work well on real-world problems. In this paper, we try to find an answer to this question by examining the performance of ten EMO algorithms including both well-known representative algorithms and recently-proposed new algorithms. First, those algorithms are applied to five artificial test suites (DTLZ, WFG, Minus-DTLZ, Minus-WFG and MaF) and three real-world problem suites. The performance of each algorithm is evaluated by the hypervolume indicator. Next, the ranking of the ten EMO algorithms is created for each problem suite. That is, eight different rankings are obtained (each ranking is for each problem suite). Then, the eight different rankings are visually compared to answer our research question. The distance between two rankings is also calculated to support visual comparison results. Our experimental results show that similar rankings of the ten EMO algorithms are obtained for the three real-world problem suites and Minus-WFG. It is also shown that the ranking for each of the three real-world problem suites is clearly different from their ranking for DTLZ. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Fatigue Driving Detection Based on Facial Features 8

Fatigue Driving Detection Based on Facial Features

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8th International conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Lou, Li Yue, Tiantian Xi'an Shiyou University School of Computer Science Shaanxi Xi'an China

ISBN: (纸本)9798350302455

In recent years, traffic accidents caused by fatigue driving are common. After a long period of continuous driving, the driver will have a dysfunction of mental and physiological functions, and his judgment and execution will be affected. The traditional fatigue detection methods are mainly based on physiological characteristics and vehicle driving information. These two detection methods are complex, require high detection accuracy and expensive equipment, and may be invasive to the driver's body, affecting the normal operation of the driver, and cannot be put into large-scale use in actual production. In this paper, we combine the methods of deep learning in the field of computer vision, based on the YOLOv5 object detection network model, using foreign public dataset YawnDD and CEW closed-eye dataset, and using multi-feature detection to detect the driver's eye and mouth fatigue features. In addition, this paper introduces a CBAM attention mechanism based on the YOLOv5 network structure to improve network performance and make fatigue detection more accurate. © 2023 IEEE.

关键词： Object detection

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Shift Aliasing Signal Data Augmentation Method for SSVEP-based BCIs 30

Shift Aliasing Signal Data Augmentation Method for SSVEP-bas...

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30th International conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Huang, Jiayang Wen, Hao Yang, Pengfei Yang, Keyi Xiong, Bang Xidian University School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an710126 China

ISBN: (纸本)9798350391916

The performance of training-based spatial filtering methods for steady-state visual evoked potential (SSVEP) decoding heavily relies on the availability of sufficient and high-quality calibration data. Limited calibration data can lead to suboptimal spatial filters and low signal-to-noise ratios in individual SSVEP templates, ultimately degrading the decoding accuracy and efficiency. To address this challenge, we propose a novel data augmentation method called Shift Aliasing Signal (SAS), which generates synthetic SSVEP epochs by estimating a mixing matrix from the available training data and applying time-shift operations while maintaining the essential features of SSVEP signals, such as their phase-locked nature and frequency-specific patterns. The SAS method introduces meaningful variations to enhance the training dataset. We integrate the SAS method with state-of-the-art training-based spatial filtering methods to create a powerful decoding framework for SSVEP-based brain-computer interface (BCI) systems. Extensive experiments on benchmark datasets demonstrate that the proposed SAS method significantly improves classification accuracy and information transfer rate, particularly in scenarios with limited calibration data, outperforming state-of-the-art training-based spatial filtering methods and other data augmentation techniques. © 2024 IEEE.

关键词： Signal to noise ratio

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