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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3103 条记录，以下是411-420 订阅

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Towards Reliable CIDS: Hybrid Reputation and Challenge Strategies for Combating Internal Attacks

Towards Reliable CIDS: Hybrid Reputation and Challenge Strat...

引用

robotics, Control and automation (ICRCA), international Conference on

作者： Yuandong Min Jingjing Zhang Sicong Liu Jia Li Furong Yu Jianjun Chen Hao Zhang National Engineering Research Center for E-Learning Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Wuhan Maritime Communication Research Institute (WMCRI) Wuhan China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Collaborative Intrusion Detection Systems (CIDS) serve as crucial firewalls, maintaining system security through necessary information exchange and integration among nodes in distributed systems. While CIDS excel at defending against external network attacks, internal threats remain a significant challenge. To identify malicious internal nodes, researchers typically adopt trust management mechanisms. However, current methods often fail to effectively take into account nodes' global reputation and historical behavior. Recognizing the similarities between distributed networks and social networks, we propose a hybrid trust computation mechanism based on social network reputation theory and develop a high-performance node clustering model. Simulated experiments conducted on a network with 50 nodes demonstrate that our method achieves higher accuracy and recall rates in identifying malicious nodes under adverse conditions compared to traditional methods, thereby enhancing the resilience of distributed networks against internal attacks.

关键词： Social networking (online) Firewalls (computing) computational modeling Intrusion detection Reliability theory Network security Trust management Robots Information exchange Resilience

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Out-of-Distribution Robustness with Deep Recursive Filters

Out-of-Distribution Robustness with Deep Recursive Filters

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Katyal, Kapil D. Wang, I-Jeng Hager, Gregory D. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781728190778

Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component of state and uncertainty estimation for robot navigation is to perform robustly under out-of-distribution noise. Traditional methods of state estimation decouple perception and state estimation making it difficult to operate on noisy, high dimensional data. Here, we describe an approach that combines the expressiveness of deep neural networks with principled approaches to uncertainty estimation found in recursive filters. We particularly focus on techniques that provide better robustness to out-of-distribution noise and demonstrate applicability of our approach on two scenarios: a simple noisy pendulum state estimation problem and real world pedestrian localization using the nuScenes dataset [1]. We show that our approach improves state and uncertainty estimation compared to baselines while achieving approximately 3x improvement in computational efficiency.

关键词： Location awareness Deep learning Uncertainty Navigation Conferences Robustness computational efficiency

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RASCAL: Robotic Arm for Sherds and Ceramics Automated Locomotion

RASCAL: Robotic Arm for Sherds and Ceramics Automated Locomo...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Wang, Deborah Lutz, Brandon Cobb, Peter J. Dames, Philip Temple Univ Dept Mech Engn Philadelphia PA 19122 USA Univ Hong Kong Fac Educ & Arts Hong Kong Peoples R China

ISBN: (纸本)9781728190778

Ceramics are one of the major sources of information about the past for archaeologists, with a typical archaeological dig unearthing 1000's of pottery fragments (sherds) each day. However, archaeologists often are not allowed to remove these sherds from their home countries. Therefore, logging data (e.g., mass, color, decoration) in the field is the only way to record valuable information about these sherds. Currently, this laborious process is done manually, using up much of the valuable time at a dig site. This project aims to automate the data collection process, freeing up archaeologists to spend their limited time in the field on other tasks, and to create a large-scale digital database of sherd information, allowing archaeologists to take advantage of new computational tools to make discoveries. The contribution of this paper is an automated system, consisting of several reconfigurable data collection stations and a robotic arm to transport objects between stations, that can rapidly generate a large database of archaeological artifacts. We validate our system in simulation, using high-resolution models of sherds. In other contexts, our system may help to expand the use of automation in materials handling, parts sorting, and more.

关键词： automation Three-dimensional displays Databases computational modeling Data collection Tools Manipulators

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Testing and Integration of Commercial Hydrogen Sensor for Ambient Monitoring Application

Testing and Integration of Commercial Hydrogen Sensor for Am...

引用

international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mohammed Faeik Ruzaij Al-Okby Thomas Roddelkopf Hartmut Ewald Kerstin Thurow Technical Institute of Babylon Al-Furat Al-Awsat Technical University (ATU) Kufa Iraq Center for Life Science Automation (celisca) University of Rostock Rostock Germany Institute of Automation University of Rostock Rostock Germany Institute of General Electrical Engineering University of Rostock Rostock Germany

Hydrogen is one of the most promising and clean energy sources, which is being used in various fields. It has a high explosive potential in the concentration range between 4 to 74.2 vol%. Thus, a suitable monitoring for the early detection of leakages is necessary to prevent serious accidents. Hydrogen is odorless, colorless, and undetectable by the senses of smell and sight. The monitoring of the gas concentrations in the indoor air is therefore of particular importance. In this paper, the testing and integration of a thermal conductivity-based hydrogen sensor is presented. The sensor was tested with hydrogen gas leakages in a chemical laboratory using specific chemical reactions. Furthermore, the sensor was integrated with an ambient monitoring sensor node that is used for the detecting of leakages of hazardous gases in chemical laboratories and industrial locations. The evaluation of the sensor performance revealed that it could be used efficiently for detecting the leakages of hydrogen gas and it is very flexible to be adapted to any system with an I2C communication bus.

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An Averaging Ensemble Method for Multiclass Brain Tumor Classification Using Transfer Learning

An Averaging Ensemble Method for Multiclass Brain Tumor Clas...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international Conference on

作者： Shahajalal Hossain Shahin Sk. Shariful Alam Shah Muhammad Azmat Ullah Dept. of Electronics & Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

A brain tumor is a disease that is caused by the abnormal growth of cells inside the brain or central spinal canal. It originates from the brain or, may spread to the brain from the other parts of the human body. While the exact causes of brain tumors remain unknown, environmental exposure and mutations in genes are the primary factors that significantly influence their progression. It is very difficult to diagnose whether the tumor is cancerous or not by analyzing the MRI images with bare eyes. For this, we need to use computer-aided diagnoses (CAD) to make the treatment easier. We have proposed an ensemble model that consists of the prediction of two deep learning models- Xception & InceptionV3 with accuracy of 97.89% and 91.56% respectively. We have used the data augmentation technique here. The proposed model gives an accuracy of 98.82%. We have also used the DenseNet121 for comparison purposes but among them, the proposed model performs better and gives high accuracy for the dataset contains about 7023 human brain MRI images which are categorized into 4 classes: glioma, meningioma, no-tumor & pituitary. The model averaging ensemble method results demonstrate that utilizing it can successfully categorize the samples that are incorrectly classified by the individual models.

关键词： Solid modeling Accuracy Magnetic resonance imaging computational modeling Transfer learning Brain tumors Medical services Predictive models Brain modeling Ensemble learning

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Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways

Video Self-Supervised Cross-Pathway Training Based on Slow a...

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2023 IEEE international Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Jie Yang, Jing Jiang, Zhou Chen, Yuehai Du, Shaoyi School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350337020

In the field of video self-supervised learning, contrastive instance learning methods suffer from a lack of semantic information, resulting in inadequate generalization in downstream tasks. Although optical flow can provide some semantic information, it requires significant computational cost prior to training. To address this, we propose a Video self-supervised Cross-pathway training model based on Slow and Fast pathways (VCSF). This model separately extracts temporal and spatial features from pure RGB video frames, and uses the complementary representations of the two pathways to conduct cross-pathway training. Additionally, we propose a motion perception module in the low-frame-rate space to enhance the network's ability to perceive rapidly changing human motion. We conducted extensive experiments in downstream missions of UCF101 and HMDB51, and obtained state-of-the-art results in models using the UCF101 data set for self-supervised pre-training, including motion recognition and nearest neighbor retrieval. © 2023 IEEE.

关键词： Cross-pathway training Self-supervised learning Slow and Fast pathways

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Modeling Affect-based Intrinsic Rewards for Exploration and Learning

Modeling Affect-based Intrinsic Rewards for Exploration and ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Zadok, Dean McDuff, Daniel Kapoor, Ashish Technion Comp Sci Dept Haifa Israel Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781728190778

Positive affect has been linked to increased interest, curiosity and satisfaction in human learning. In reinforcement learning, extrinsic rewards are often sparse and difficult to define, intrinsically motivated learning can help address these challenges. We argue that positive affect is an important intrinsic reward that effectively helps drive exploration that is useful in gathering experiences. We present a novel approach leveraging a task-independent reward function trained on spontaneous smile behavior that reflects the intrinsic reward of positive affect. To evaluate our approach we trained several downstream computer vision tasks on data collected with our policy and several baseline methods. We show that the policy based on our affective rewards successfully increases the duration of episodes, the area explored and reduces collisions. The impact is the increased speed of learning for several downstream computer vision tasks.

关键词： Computer vision automation Conferences computational modeling Reinforcement learning Task analysis

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Practical and Accurate Generation of Energy-Optimal Trajectories for a Planar Quadrotor

Practical and Accurate Generation of Energy-Optimal Trajecto...

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IEEE international Conference on robotics and automation (ICRA)

作者： Morbidi, Fabio Pisarski, Dominik Univ Picardie Jules Verne MIS Lab Amiens France Polish Acad Sci IPPT PAN Inst Fundamental Technol Res Warsaw Poland

ISBN: (纸本)9781728190778

Motivated by the limited flight time of battery-powered multi-rotor UAVs, in this paper we address the problem of generating energy-optimal trajectories for a planar quadrotor. More specifically, by considering an accurate electrical model for the brushless DC motors and rest-to-rest maneuvers between two predefined boundary states, we explicitly compute the minimum-energy curves by adopting a free and a fixed end-time optimal control formulation. The numerical solution of these optimal control problems hinges upon a simple yet effective indirect projected gradient method. Simulation experiments illustrate the theory in a variety of realistic flight scenarios.

关键词： Gradient methods automation Brushless DC motors computational modeling Conferences Optimal control Fasteners

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Model-Based Shape Estimation and Closed-Loop Control of Magnetic Continuum Robot Based on Monocular Vision

Model-Based Shape Estimation and Closed-Loop Control of Magn...

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IEEE international Conference on robotics and Biomimetics

作者： Junfeng Zhu Yuanrui Huang Jian Chen Hongbin Liu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre of AI and Robotics Hong Kong Institute of Science Innovation Chinese Academy of Sciences School of Biomedical Engineering and Imaging Sciences King's College London UK

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgeries. However, the submillimeter dimensions of MCRs poses significant challenges for integrating additional sensors. An alternative approach is shape estimation from images, yet the lack of precise MCR kinematics can make this process time-consuming. In this work, we propose a novel MCR design featuring a rectangular tip capable of specific planar bending, along with a corresponding kinematic model and a mechanical model. Additionally, we introduce a model-based shape estimation method that enables the estimation of the MCR's shape from a single-view image without the need for external markers. Experimental results demonstrate that our method can estimate an MCR's shape with a mean absolute error (MAE) of $\mathbf{0.58}\pm \mathbf{0.41}\mathbf{mm}$ within 85ms, facilitating real-time application. Furthermore, closed-loop control based on this method has achieved an end position error below 11% of the tip's total length, highlighting the accuracy and practicality of our approach for surgical interventions.

关键词： Solid modeling Shape computational modeling Magnetic resonance imaging Estimation Kinematics Robot sensing systems Real-time systems Continuum robots Magnetosphere

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Bipedal Robot Gait Control Strategies Based on Reinforcement Learning

Bipedal Robot Gait Control Strategies Based on Reinforcement...

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robotics & Intelligent Manufacturing Technology (ISRIMT), international symposium on

作者： Zhengquan Mao Jing Li Nanyan Shen Jiawen Ding Bin Zhou Chong Li School of Mechatronic Engineering and Automation Shanghai University Shanghai China Fourier Intelligence Shanghai China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

This paper presents a reinforcement learning-based gait control strategy for bipedal robots. The work focuses on enabling the robot to adapt to complex terrains without relying on visual information. By leveraging the neural network's fitting capability in a simulated environment, the robot estimates privileged information from limited proprioceptive data. The approach is based on reinforcement learning fundamentals and includes a teacher-student policy architecture. We design a series of reward functions to achieve velocity tracking, periodic gait phases, accurate foot traj ectories, and smooth locomotion. Domain randomization is employed to bridge the gap between simulation and reality. Experiments using Proximal Policy Optimization show promising results, and the future work aims to deploy the policy on a real humanoid robot and explore full-body walking strategies.

关键词： Legged locomotion Visualization Accuracy Fitting Humanoid robots Propioception Reinforcement learning Manufacturing Optimization Foot

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