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automation, Control and Robotics Engineering (CACRE), International Conference on

作者： Hongmin Wang Meng Jiang Longhua Wu Junlin Huang Zengxi Pan Dagang Li Yingying Song Hui Huang School of Mechanical and Automation Engineering Institute of Carbon Peaking and Carbon Neutralization Wuyi University Jiang Men China School of Mechanical and Automation Engineering Wuyi University Jiang Men China Robotics and Welding Automation Laboratory University of Wollongong Australia School of Computer Science and Engineering Macau University of Science and Technology Macau China Faculty of Innovation and Entrepreneurship Wuyi University Jiang Men China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This study aims to solve the dual robotic arm collaboration problem by proposing a method based on an adaptive conductivity control algorithm for nondestructive grasping and handling of unknown manipulated objects by dual robotic arms. The method is capable of dynamically tracking the intended force of the unknown entity while maintaining the desired trajectory and compensating for the error in the state of the unknown object. In this paper, adopting a two-arm collaborative robotic arm motion planning method oriented to the manipulated object, the kinematics of a singular appendage is calculated by closed-loop constraints. The collaborative control strategy of the dual robotic arms for the operated object is established, which facilitating the pursuit of the intended force and the rectification of the positional path deviation induced by external interference. Through simulation and experimental verification, the method demonstrates satisfactory performance regarding the error in tracking position and the steady state deviation in force, offering a viable solution for attaining heightened precision in tracking both position and force.

关键词： Trajectory tracking Force Collaboration Kinematics Manipulators Trajectory Steady-state

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Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

arXiv

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arXiv 2023年

作者： Wang, Yuntao Su, Zhou Xu, Qichao Luan, Tom H. Lu, Rongxing School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Faculty of Computer Science University of New Brunswick Fredericton Canada

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and user cost model in dynamic game scenarios, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mako:A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth Graph mining Formation mechanism

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features

Diabetic Retinopathy Image Classification Using Machine Lear...

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International Symposium on Electrical and Electronics Engineering (ISEEE)

作者： Mihaela Miron Anisia Culea-Florescu Simona Moldovanu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task.

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Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

Medical Visual Prompting (MVP): A Unified Framework for Vers...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Sun Yat-Sen University Cancer Center Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (纸本)9781665410205

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. © 2024 IEEE.

关键词： Image segmentation

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Emotion Recognition in Human Thermal Images with Artificial Intelligence Technology

Emotion Recognition in Human Thermal Images with Artificial ...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Sorin Pavel Simona Moldovanu Dorel Aiordachioaie Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy.

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Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools 15th

Minimization of Nonlinear Energies in Python Using FEM and...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Béreš, Michal Valdman, Jan Institute of Geonics of the Czech Academy of Sciences Studentská 1768 Ostrava708 00 Czech Republic Department of Applied Mathematics VSB - Technical University of Ostrava 17. listopadu 15/2172 Ostrava708 00 Czech Republic Institute of Information Theory and Automation of the Czech Academy of Sciences Pod vodárenskou věží 4 Prague18208 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic

ISBN: (纸本)9783031857027

This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python’s advanced computational tools, such as automatic differentiation. We demonstrate Python’s streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python’s efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Python

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Design of ship course-keeping system via adaptive control techniques 26

Design of ship course-keeping system via adaptive control te...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Zwierzewicz, Zenon Dorobczynski, Lech Sosnowski, Mariusz Jaszczak, Slawomir Maritime University of Szczecin Department of Marine Automation ul. Willowa 2 Szczecin71-650 Poland West Pomeranian University of Technology Faculty of Computer Science and Information Technology ul. Zolnierska 49 Szczecin71-210 Poland

The paper discusses the problem of ship autopilot design based on the input-output feedback linearization method combined with the adaptive control techniques. At first, the nonlinear ship model of the Norrbin type is linearized with the use of simple system nonlinearity cancellation. Since the exact values of the model parameters are not known to overcome the resulting inaccuracies, these parameters are tuned in such a way as to guarantee the asymptotic stability of the system. Some novelty is that by treating the load disturbance as an additional, unknown parameter of the system, we do not need to introduce an extra integral action to deal with the appearing steady-state error. The simulations of the ship course change process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental disturbances. An important feature of this approach is also the elimination of the tuning problem. © 2022 The Authors. Published by Elsevier B.V.

关键词： Three term control systems

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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