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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24667 条记录，以下是251-260 订阅

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Experimental Validation of Intelligent Ad Hoc System for Sea Surface Velocity Approximation

Experimental Validation of Intelligent Ad Hoc System for Sea...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Karlo Jakac Luka Lanča Ante Sikirica Stefan Ivić Faculty of Engineering University of Rijeka Rijeka Croatia Center for Advanced Computing and Modelling University of Rijeka Rijeka Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper presents the validation of a smart ad hoc system designed to approximate the sea surface velocity field using measurements obtained from multiple floating sensors. Recognizing that point data alone cannot fully capture the domain's circulation, we propose a method that uses a simplified two-dimensional flow model as a surrogate for submesoscale flow, reconstructing the entire velocity field from the scattered data. To ensure reliable flow dynamics, the approach addresses the problem as a model-fitting process, adjusting the boundary conditions of numerical simulations to align the flow with measured data. Experimental validation was performed with the collected measurements, which involved the deployment of Global Positioning System (GPS) drifters in the Kvarner Bay area. The results highlight the system's adaptability to various domains, measurement types, and conditions, demonstrating its effectiveness for real-world submesoscale applications where an approximate velocity field is acceptable.

关键词： Sea surface Surface reconstruction Sea measurements Data models Numerical models Velocity measurement Wind forecasting Optimization Intelligent sensors Global Positioning System

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Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization 27th

Delving into Feature Space: Improving Adversarial Robustness...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Cheng, Zhen Zhu, Fei Zhang, Xu-Yao Liu, Cheng-Lin State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China School of Artificial Intelligence UCAS Beijing China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong

ISBN: (纸本)9783031783944

The study of adversarial examples in deep neural networks has attracted great attention. Numerous methods improve adversarial robustness via shrinking the gap of features between natural examples and adversarial examples. Nevertheless, the role of individual features in adversarial robustness has not been explored adequately. In this paper, we delve into this problem from the perspective of spectral analysis in feature space. We find that while standardly trained deep models have features distributed dominantly along eigenvectors with large eigenvalues, eigenvectors with smaller eigenvalues are more sensitive to adversarial attacks. We attribute this phenomenon to the dominance of the top eigenvalues, linked to the concept of intrinsic dimensionality. The extracted features possess a small intrinsic dimensionality, enhancing generalization but resulting in the model overlooking diverse features. We propose a method called Feature Spectral Regularization (FSR) to penalize the largest eigenvalue, so as to spread the distribution of eigenvalues. Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues, increase the intrinsic dimensionality, and improve adversarial robustness on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Feedback Control Based on Trust for Varied automation Transparency

Feedback Control Based on Trust for Varied Automation Transp...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Ceres Dbritto Joel Lopes Krishnan Bandyopadhyay Nandan Banerji Ishani Mishra Ankit Kandpal Independent Reseacher St Louis USA Independent Reseacher Redmond USA Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Computer Science and Engineering New Horizon college of Engineering Bengaluru Karnataka India Department of ECE Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Interactions between people and automation depend heavily on human confidence in the latter. Lack of trust can cause people to not use automation, while too much trust can cause them to put their faith in a flawed autonomous system, which could have detrimental effects on them. Transparency and trust in automation are vital for the usefulness and security of human-robotization frameworks. The majority of the research that has been done on the trust that human operators have in robotics and automation has been an ongoing variable that has received little attention. paid to how trust changes over time. In order to fill this research void, we offer a study that examines how trust is dynamic. Using real-time trust measurements, we first discovered options to quantify trust of entirety, which we described as a metric that considers trust throughout the entire human-automation interaction experience. In this study, a feedback control strategy for powerfully changing mechanization straightforwardness and transparency concurring on client trust is analysed. The proposed approach tweaks mechanical way of behaving and correspondence by incorporating an input control component with a trust assessment model. The adequacy of the feedback control framework in improving errand execution and trust adjustment under different levels of computerization straightforwardness and transparency is checked utilizing MATLAB reenactments.

关键词： Measurement Robust control automation Computational modeling Mathematical models Robustness Real-time systems Feedback control Security MATLAB

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AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-childhood Education

AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-c...

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international conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Asif Mahmud Tahsin Faisal M. Akhtaruzzaman Jamal Uddin Tanvin Sachitra Halder Fahim Shahryer Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The smart Dialogic Alphabet Zone Board (DAZ-Board) aims to address the issue of learning process for pre-childhood education among the children. It is importance to facilitate a healthy and effective environment for early childhood education in shaping a child’s cognitive development, language acquisition, and literacy skills. Traditional teaching methods often fail to engage young learners effectively, leading to limited understanding and retention. On the other hand, dependencies on mobile devices raise the physical and mental health issues. This study introduces an interactive, auditory, and multisensory game board providing a wonderful and healthy experiences in tailoring the developmental needs of children. The objective of this research is to design and develop an interactive alphabet board with necessary sensors, electronics, and controller modules. Moreover, designing a Machine Learning (ML) model for voice-based alphabet recognition is one of the important contributions of this research. Among the results of different ML models, Naïve Bayes Classifier outperforms the most with accuracy ≈ . %. The proposed system will facilitate the preschoolers to learn in modern and interactive way, while keeping away from being addicted to the mobile devices.

关键词： Pediatrics Visualization Accuracy Scalability Education Games Speech recognition Sensors Smart phones Systematic literature review

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A GNN Based Voltage Sensitivity Perception Method for Distribution Networks with Variable Topology

A GNN Based Voltage Sensitivity Perception Method for Distri...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Keyang Li Chuanhuai Ji Shuona Li Yajie Tang College of Electrical Engineering Zhejiang University Hangzhou China State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Traditional data-driven voltage sensitivity analysis methods exhibit limited adaptability in response to network topology changes, which need to retrain the neural networks according to the updated topology information. To address this issue, this paper proposes a voltage sensitivity perception method based on graph neural networks (GNN). By embedding prior topology knowledge for information propagation, the method can effectively capture the feature mapping patterns of various topologies, thereby demonstrating improved model generalization ability in unknown topology scenarios. Matlab/Simulation results show that, compared to conventional data-driven algorithms, the proposed method achieves faster and more accurate sensitivity calculations in time-varying topology scenarios.

关键词： Adaptation models Sensitivity Accuracy Voltage measurement Network topology Fitting Graph neural networks Mathematical models Data models Topology

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Smooth Particle Swarm Fluid Simulation Algorithm Based on Graph Neural Network

Smooth Particle Swarm Fluid Simulation Algorithm Based on Gr...

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Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Yixuan He Huifu Luo School of Mechanical and Vehicle Engineering Beijing Institute of Technology Haidian Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Smoothed Particle Hydrodynamics (SPH), as a fluid simulation technique, has significant advantages in dealing with complex boundary and large deformation simulation problems, and has been widely applied in the fields of film and television animation special effects, virtual reality, metaverse, and digital twins. However, traditional methods combining SPH with neural networks have limitations in capturing local area details. To address these issues, this paper proposes an improved model - Adaptive Search Smoothed Particle Network (ASSPN). ASSPN integrates deep learning technology, optimizes the neural network structure, and enhances computational accuracy and detail simulation accuracy. Compared to traditional SPH and Neural Particle Method (NPM), ASSPN demonstrates higher robustness in handling boundary conditions and complex application scenarios, offering greater spatial discretization flexibility in the field of fluid simulation, and is expected to become a powerful tool for fluid dynamics modeling.

关键词： Training Deep learning Solid modeling Adaptation models Computational modeling Fluid dynamics Predictive models Data models Robustness Graph neural networks

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AI-Driven Penetration Testing: Automating Exploits with LLMs and Metasploit-A VSFTPD Case Study

AI-Driven Penetration Testing: Automating Exploits with LLMs...

引用

international conference on New Trends in Computing Sciences (ICTCS)

作者： Maher Salem Mohammad Mrian Department of Informatics King's College London London United Kingdom Department of Cybersecurity Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Penetration testing, a critical cybersecurity practice, is often bottlenecked by manual exploit selection and payload crafting. We propose a novel framework integrating Large Language models (LLMs) with Metasploit to automate vulnerability analysis, exploit selection, and payload customization. Our system dynamically adapts to target defenses, demonstrated through a case study on VSFTPD 2.3.4 (CVE-2011-2523), where it autonomously generates and executes a reverse shell payload. Evaluated in controlled environments, our approach significantly enhances efficiency, accuracy, and adaptability, outperforming traditional manual methods. This work highlights the transformative potential of AI-driven automation in cybersecurity while raising important ethical and operational questions for future red-teaming and defensive strategies.

关键词： Ethics Accuracy automation Large language models Scalability Training data Manuals Penetration testing Payloads Testing

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Application of Machine Learning methods for Production Data Analysis

Application of Machine Learning Methods for Production Data ...

引用

international Russian Smart Industry conference (SmartIndustryCon)

作者： Igor Volkov Yuri Andreev Faculty of Control Systems and Robotics ITMO University Saint- Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Ensuring the quality of CNC-machined parts is a challenging task in modern manufacturing due to frequent shifts in production conditions and the high costs of traditional inspection methods. In this study, we propose a data-driven approach that integrates machine learning (ML) models with statistical acceptance sampling to dynamically optimize quality control processes. Production data from ERP, MES, and IIoT systems were analyzed with Gradient Boosting model. By continuously retraining the model on newly completed batches, the system adapts to evolving conditions, maintaining predictive accuracy. A key innovation of this work is the timely adjustment of sampling parameters (e.g., sample size, acceptance number) according to predicted defect levels. This adaptive strategy conserves inspection resources when defect risks are low and intensifies monitoring when risks are high. The results demonstrate a marked reduction in inspection costs and a robust capacity to detect defects promptly, aligning with industry goals of minimizing scrap and rework. Overall, this study validates the effectiveness of applying machine learning methods to production data analysis, laying a foundation for future enhancements in adaptive, proactive quality control systems.

关键词： Industries Adaptation models Technological innovation Data analysis Costs Process control Production Quality control Inspection Predictive models

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An Embedding Algorithm Based on Neighborhood Aggregation for Attribute Graphs

An Embedding Algorithm Based on Neighborhood Aggregation for...

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international conference on Computer and automation Engineering, ICCAE

作者： Dongming Chen Shuyue Zhang Yumeng Zhao Mingzhao Xie Dongqi Wang Software College Northeastern University Shenyang China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Attribute graph embedding algorithms based on graph neural networks aggregate information about the neighborhood of a target node to update the embedding of the target node, i.e. neighborhood aggregation. The shallow network architecture of existing models limits the generalization ability of the models on large datasets, and the number of nodes increases exponentially with the number of graph neural network layers, resulting in gradient explosion and the need for faster model training methods. In addition, the multiple iteration process of neighborhood aggregation changes the original attribute information of the nodes, especially when the attributes of the target node and the attribute information of the neighborhood nodes are extremely different, which makes the target node attributes deviate significantly from the original attributes. Excessive neighborhood aggregation mixes different clustered nodes, causing all nodes to converge to the same embedding, resulting in excessive smoothing. In order to solve the above problems while considering the supervised learning of the model and the utility of neighborhood aggregation, this paper proposes a subgraph sampling method based on graph sampling for attribute graph data pre-processing, and an attribute graph embedding algorithm for improving neighborhood aggregation using an adaptive learning strategy.

关键词： Training Adaptive learning Smoothing methods Supervised learning Network architecture Sampling methods Graph neural networks Explosions Data models Classification algorithms

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Performance Analysis of ANFIS and DRL Based Speed Controllers for Sensorless Drives

Performance Analysis of ANFIS and DRL Based Speed Controller...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Kasim M. Al-Aubidy Abdullah F. Al-Saoudi Mechatronics Eng. Dept. Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Permanent magnet synchronous machines (PMSMs) are of great interest in automation and robotics applications due to their small size, high efficiency, and low maintenance requirements. However, these motors face challenges in controlling their speed and position, such as precise rotor detection. This situation is essential in many Industrial applications. Conventional controllers such as PID controllers are commonly used, but their performance is often affected by changes in system dynamics, necessitating updating of controller parameters based on the accurate mathematical model of the motor. This research aims to investigate several intelligent control strategies that do not rely on the mathematical model of the motor. The performance of the drive system will be evaluated using three intelligent controllers: a neural network controller, a neural fuzzy inference system (ANFIS) controller, and a deep reinforcement learning (DRL) controller. The performance of these smart controllers will be compared with that of the PID controller. The results presented in this paper demonstrate the ability of the proposed smart controllers to regulate the speed of PMSMs compared to a PID controller. Specifically, the ANFIS-based controller shows the best performance in terms of maximum overshoot, while the DRL controller demonstrates the best settling time.

关键词： automation Service robots System dynamics Velocity control Motors Robot sensing systems Control systems Mathematical models Maintenance Time factors

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