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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Li, Jinyang Tong, Jiahui Gao, Guangyu Xu, Wenbin School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Optical Radiation Laboratory Beijing Institute of Environmental Characteristics Beijing100854 China

ISBN: (纸本)9781450398572

Traditional deep learning models firmly rely on a large amount of labeled data during pre-training. Whereas it lacks generalization in the face of unfamiliar categories. Recently, few-shot learning is a hot topic in computer vision to classify unseen classes with limited labels. A representative approach is to extract features from the support and query sets, respectively, and compare similarities via metric learning. However, convolutional neural networks often focus only on a local region and ignore the global region, which severely reduces the accuracy of the matching. Specifically, in this paper, we pile lightweight attention-based blocks in the embedding module, which combines an adaptive kernel size 2D convolutional network with a cross-channel attention mechanism to encode multi-scale features and implicitly increase the receptive field. The SE-relation module chooses to construct learnable non-linear comparators to compare the relationship utilizing channel information. Finally, we show experimental results on standard few-shot testing benchmarks such as mini-ImageNet and tiered-ImageNet to demonstrate effectiveness. © 2023 ACM.

关键词： Image classification

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Research of Extended Dimension Embedded CKF-SLAM Base on Truncated Singular Value Decomposition

Research of Extended Dimension Embedded CKF-SLAM Base on Tru...

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International Conference on Cybernetics, Robotics and control (CRC)

作者： Shibo Zheng Kai He Shuo Meng Fangchencan Liu Qiang Guo School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang City School of Computer Science and Engineering Shenyang Jianzhu University Shenyang City

In response to the problem of the filtering error of the CKF algorithm increasing linearly with the dimensionality of the state space, resulting in difficulty in propagating multiplicative noise, and the instability of the system caused by outliers in the system state driven model and observation data, this paper proposes an Extended Dimensional Embedded Cubature Kalman Filter algorithm based on the Truncated Singular Value Decomposition (TSVD-AECKF). Firstly, Singular Value Decomposition (SVD) is used instead of Cholesky Decomposition in the CKF algorithm to suppress the non-positivity of the system state covariance matrix; Considering the impact of small singular values on stability, this paper adopts the truncation method and provides a method for determining the truncation threshold; Secondly, the system noise is added to the state variable, and the embedded cubature criterion is used to improve the traditional CKF while expanding its dimensions; Finally, through simulation experiments, TSVD-AECKF was compared with other SLAM methods, and the results showed that this method can effectively suppress positioning errors that increase with the dimension of the state space, enhance the filter's ability to resist noise data interference, and thereby improve the robustness and stability of the mapping.

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Distributionally Robust control for Chance-Constrained Signal Temporal Logic Specifications 63

Distributionally Robust Control for Chance-Constrained Signa...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. Soudjani, Sadegh Max Planck Institute for Software Systems Kaiserslautern Germany Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Southern California Viterbi School of Engineering Thomas Lord Department of Computer Science Los Angeles United States

ISBN: (纸本)9798350316339

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method. © 2024 IEEE.

关键词： Stochastic systems

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A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous Systems

A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous S...

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Software engineering and computer science (CSECS), International Conference on

作者： Xiaoyan Wu Dali Xu Fangfa Fu Bing Yang Hongyang Liu Kanglong Liu Xiyu Ding Chao Wu Na Niu School of Computer and Control Engineering Northeast Forestry University Harbin China School of Astronautics Harbin Institute of Technology Harbin China School of Computer Science Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Clear outdoor images are essential for autonomous driving and accurate target detection, especially in haze. The majority of algorithms are unable to adequately address the issue of dehazing, resulting in a range of distortions, particularly in the sky area. This paper proposes an advanced dehazing algorithm for enhancing sky-area visuals (ESV). We segment the image into sky and non-sky areas, with atmospheric light levels being determined within the sky area. To enhance the recovery of the sky region, we suggest fine-tuning the sky's transmission to a predetermined constant value. Ultimately, the dehazed image is retrieved utilizing the atmospheric scattering model. Extensive experiments have shown that our proposed algorithm outperforms alternative methods, increasing PSNR by up to 1.3733%, 1.6360%, 2.4169%, 0.9512%, SSIM by up to 4.8995%, 0.6281%, 6.5335%, 8.7165%, enhancing the visuals of sky-area, compared to DCP, CAP, HC-CEP and AOD-Net.

关键词： Visualization Atmospheric modeling Software algorithms Scattering Reinforcement learning Object detection Distortion Rendering (computer graphics) Robustness Software engineering

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Real-time Hybrid System Identification with Online Deterministic Annealing

arXiv

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arXiv 2024年

作者： Mavridis, Christos N. Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We introduce a real-time identification method for discrete-time state-dependent switching systems in both the input-output and state-space domains. In particular, we design a system of adaptive algorithms running in two timescales;a stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale and estimates the mode-switching signal, and an recursive identification algorithm runs at a faster timescale and updates the parameters of the local models based on the estimate of the switching signal. We first focus on piece-wise affine systems and discuss identifiability conditions and convergence properties based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for switched systems, the proposed approach gradually estimates the number of modes and is appropriate for real-time system identification using sequential data acquisition. The progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off. Finally, we address specific challenges that arise in the application of the proposed methodology in identification of more general switching systems. Simulation results validate the efficacy of the proposed methodology. © 2024, CC BY.

关键词： Stochastic systems

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Research on Enhanced YOLOv8 Gesture Recognition Method for Complex Environments*

Research on Enhanced YOLOv8 Gesture Recognition Method for C...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuai Yuan Xiangjie Kong Shuai Zhang School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

This study proposes a gesture recognition method, named DF-YOLOv8s to address the issue of low recognition rate under complex environments. Our approach firstly replaces the SPPF module with the AIFI module to enhance feature extraction effectiveness by using attention-based internal scale feature interaction. Then we design a novel ZD feature fusion network according to the ASF-YOLO network structure, which improves our capability to extract and fuse features in images, thereby enhancing gesture recognition accuracy under complex environments. This improvement comprehensively considers features of different sizes, occlusion and uneven illumination that may result in small target loss. Finally, experiments are performed on our self-built dataset from publicly available datasets NUS-II and HaGRID, which riches lighting contrast, skin-colored background, and foreground occlusion characteristics. Our experimental results represent mAP50 score of 95.4% with a 2.8% enhancement over YOLOv8s. And also we have performed contrasted tests with other algorithms, the results illustrated the validity of the proposed method.

关键词： Human computer interaction Accuracy Fuses Convolution Lighting Gesture recognition Feature extraction

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Design of Personalization Warehouse Management Platform Based on SaaS Model 25

Design of Personalization Warehouse Management Platform Base...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Guo, Qi Sun, Hongbo Ji, Wen Yantai University School of Computer and Control Engineering Shandong Yantai264005 China Institute of Computing Technology Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9781665405270

SaaS is widely used in the fields of operation management, business process outsourcing, data analysis, and information security. However, with the improvement of the degree of information, the generalized SaaS platform is unable to satisfy the requirements of enterprise personalization. SaaS products face great challenges in personalized customization technology due to the application of multi-tenant architecture. The challenges include multi-tenant customization in SaaS model, data isolation during customization, and mapping of multi-tenant virtual warehouse locations to actual locations. Therefore, we decompose personalization technology into metadata driver, cloud data placement and mapping mechanism for research. We decompose the personalized technologies into metadata drive, cloud data placement, and mapping mechanism. In order to solve the problem that traditional personalization customization is unable to be applied in the SaaS field, we propose a multi-tenant personalized warehousing mode architecture, designed a personalization warehouse management platform based on SaaS model, and realized SaaS-based data isolation, interface customization, rapid positioning, and on-demand customization services. Experimental results show that the proposed multi-tenant personalized warehouse model can achieve data isolation and customization, and reflect the advantages of highly automated warehousing, shared storage resources, and on-demand customization in terms of warehouse management, data security, and user experience. © 2022 IEEE.

关键词： Metadata

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Community Search Based on Containment control of Multi-Agent System with Opinion Leaders 43

Community Search Based on Containment Control of Multi-Agent...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Siyang Li, Tong Shao, Jinliang Zheng, Wei Xing University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9789887581581

Community search is the problem of identifying the community in which a given node resides. Different from traditional community search methods based on specific topological structures, this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community formation. Firstly, the Breadth-First Search method is used to locally sample the network. Secondly, containment control of multi-agent system is applied to obtain the final state of each node for local community partitioning. Finally, grounded in practical considerations, an effective leadership transfer mechanism is proposed to update the results and enhance search precision. Through experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms, the effectiveness and rationality of our algorithm are demonstrated. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Image segmentation

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Parameter fault estimation in distributed heating/cooling systems 7

Parameter fault estimation in distributed heating/cooling sy...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Kurniawan, Wijaya Hangos, Katalin M. Marton, Lorinc Faculty of Computer Science University of Brawijaya Malang Indonesia Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Institute for Computer Science and Control Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Targu Mures Romania

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon. © 2022 ACM.

关键词： Parameter estimation

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