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检索条件"任意字段=Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering"

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A Classification Method for Unordered Steel Scrap Images Based on Hybrid Convolutional Networks 24

A Classification Method for Unordered Steel Scrap Images Bas...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Yu, Yan School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798400710834

With the development of deep learning and image recognition technologies, these methods have found widespread applications across various fields. However, the annotation of industrial application-specific data, such as steel scrap images, remains challenging due to the unordered nature of the images, which often requires domain experts to perform the annotation. To address this, we propose a feature learning method for unordered images based on hybrid convolutional networks. Utilizing a preprocessed steel scrap dataset, we employ a cascaded convolutional network to capture deep correlation features within such unordered images while enhancing their fine-grained structures. A self-supervised approach is used to train the encoder, which is subsequently fine-tuned using a minimal set of labeled samples to complete the classification and annotation of steel scrap. Experimental results on four different categories of steel scrap annotation demonstrate that the proposed method achieves accuracies of 60.47%, 61.24%, 23.45%, 62.46%, respectively, verifying the effectiveness of the method. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Research on Reliability Improvement and Index Structure Optimization Based on B+ Trees 24

Research on Reliability Improvement and Index Structure Opti...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Yu, Yan Tang, Junmiao Zhang, Hesong Zhang, Hesheng School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798400710834

In aerospace systems, B+ trees are typically used for data management and indexing. However, traditional B+ trees have issues with data reliability and slower indexing speeds when handling massive data volumes. To address these issues, an improved B+ tree structure is proposed. This improvement involves a custom memory pool to manage the creation and deletion of tree nodes and employs a dynamic optimization algorithm to reduce the height of the B+ tree. Additionally, an automatic snapshot and logging system is introduced to ensure the recoverability of the B+ tree in case of system failures, enhancing its performance and reliability in complex environments. Comparative experiments indicate that the improved B+ tree achieves a 14.02% increase in efficiency for node insertion operations, a 27.39% speed improvement in searching for specific keys, and reduced memory consumption due to the dynamic optimization algorithm, demonstrating significant advantages in memory-constrained aerospace systems. © 2024 Copyright held by the owner/author(s).

关键词： Memory management

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Construction of an AI Course Cluster System for robotics Specialization Based on PBL Framework 3

Construction of an AI Course Cluster System for Robotics Spe...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Wang, Xiao Chu, Hao Ji, Peng Yu, Xiaosheng College Of Information Science and Engineering Northeastern University Liaoning Shenyang China Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798400712692

Traditional AI-related courses in robotics major are not related closely to the latest technological advancements. In order to explore a better approach in the teaching system, this paper introduces a new AI course cluster system. The system conducts the PBL teaching structure and designs a tailored AI course cluster for robotics students. An organized and project-based teaching model is established too. We also conduct case studies to the students in the robotics major. The experimental results verify the effective of the proposed cluster system. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： AI courses cluster system PBL robotics major

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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A Hybrid Method for Improving SOC Prediction in Lithium-Ion Batteries by Combining EDNN with WOA 3

A Hybrid Method for Improving SOC Prediction in Lithium-Ion ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Indumathi, V. Gopalakrishnan, R. Vijayakumar, C. Pavithran, A. Kamalesh, M. Balaji, M. K S Rangasamy College of Technology Faculty of Mechatronics Engineering Tamil Nadu Namakkal India K S Rangasamy College of Technology Faculty of Electrical and Electronics Engineering Tamil Nadu Namakkal India K S Rangasamy College of Technology Tamil Nadu Namakkal India

ISBN: (纸本)9798331509675

The safe and effective functioning of lithium-ion (Li-ion) batteries depends on accurate State of Charge (SOC) prediction. In order to increase the accuracy of SOC estimation, this study suggests a unique hybrid model that combines the Whale Optimization Algorithm (WOA) with Enhanced Deep Neural Networks (EDNN). Complex, non-linear connections between battery characteristics including voltage, current, temperature, and time are well captured by the EDNN. WOA ensures improved generalization and quicker convergence by optimizing the EDNN hyperparameters. Real-world battery datasets are used to validate the model, and metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to assess performance. According to the results, the EDNN-WOA hybrid model improves reliability under a range of operating situations by drastically lowering prediction errors when compared to conventional machine learning techniques. This method shows how cutting-edge deep learning and metaheuristic optimization approaches can be combined to improve the performance and longevity of Li-ion batteries and aid in the creation of intelligent battery management systems. © 2025 IEEE.

关键词： State of charge

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A Survey of 3D Gaussian Splatting: Optimization Techniques, Applications, and AI-Driven Advancements 3

A Survey of 3D Gaussian Splatting: Optimization Techniques, ...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Santosh Reddy, P. Abhiram, H. Archish, K.S. B.N.M. Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331515911

3D Gaussian Splatting (3DGS) has emerged as a transformative technique for real-time, high-quality 3D rendering. This paper surveys the advancements in 3DGS, focusing on optimization strategies, applications across diverse domains, and AI-driven innovations that enhance its utility. Optimization techniques, such as Mini-Splatting and sparsification frameworks, address challenges like computational overhead and memory inefficiency while maintaining visual fidelity. AI has further expanded the capabilities of 3DGS through frameworks like SceneTeller, ART3D and GaussianEditor, enabling text-to-3D generation, object-level manipulation, and artistic scene creation with multi-view consistency. Applications of 3DGS range from autonomous driving and robotics to virtual and augmented reality, highlighting its adaptability and efficiency. This survey underscores the importance of continued research to refine 3DGS's scalability and applicability, solidifying its role at the intersection of computer graphics and artificial intelligence. © 2025 IEEE.

关键词： Augmented reality

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A Hybrid Intrusion Detection System using Explainable AI for Enhanced Accuracy and Transparency 3

A Hybrid Intrusion Detection System using Explainable AI for...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Hemalatha, A. Vimal Kumar, M.N. Graf, F. Twinkle Anlin Sahaya Infant Tinu, M. Pavithra, P. Suresh, R. E.G.S. Pillay Engineering College Department of Information Technology Nagapattinam611002 India Sona College of Technology Department of Mechatronics Engineering Salem636005 India Madanapalle517325 India Rohini College of Engineering and Technology Department of Biomedical Engineering Anjugramam629401 India Tjs Engineering College Department of Computer Science and Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India Tjs Engineering College Department of Electronics and Communication Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India

ISBN: (纸本)9798331509675

Intrusion detection system is highly effective and easy to understand in the situation of ubiquitous cyber threats. The trustworthiness and interpretability of traditional intrusion detection systems are balanced due to their dependence on transparent deep learning (DL) and Machine Learning (ML)methods. To overcome these issues, this research proposes a hybrid IDS which makes use of Explainable AI(XAI). An integration of ML methods like random forest and support vector machine with DL methods like CNN ensure efficient detection accuracy in the system. The decision-making process can be better efficient with the help of XAI methods like SHAP and LIME, which increases transparency. The CICIDS 2017 dataset is used to validate the feature extraction of framework, model training and interpretability components. The result show that proposed method performs better with 98.5% accuracy rate and better precision, recall and F1-socre when compared to existing methods. This method provides useful data for securing networks while reducing the number of false positives and negatives. Through the integration of advanced machine learning/deep learning methods with XAI, the proposed IDS ensures dependability, openness and practicality. © 2025 IEEE.

关键词： Adversarial machine learning

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AI Powered Digital Twin for Rail Damage Prediction 3

AI Powered Digital Twin for Rail Damage Prediction

引用

3rd international conference on Rail Transportation, ICRT 2024

作者： Bernal, Esteban Ding, Haohao Wang, Wenjian Spiryagin, Maksym Ahmad, Sanjar Wu, Qing Rahaman, Mohammad Cole, Colin Centre for Railway Engineering Central Queensland Univ. Brisbane Australia Tribology Research Institute State Key Laboratory of Rail Vehicle System Southwest Jiaotong Univ. Chengdu China Centre for Railway Engineering Central Queensland Univ. Rockhampton Australia

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Rail digital twins (DT) can enhance efficiency in railway operations as they have the potential to generate more accurate predictions on rail degradation. Increased accuracy is achieved by DTs through combining longitudinal train dynamics, multibody dynamics, traction mechatronics, material damage, and friction, among other models. These models are computationally intensive and are not feasible for routine railway operations, which involve constant variations in train configuration, vehicle types, speed restrictions, etc. This paper introduces an AI leveraged railway DT to predict rail damage along a railway track. The DT is implemented through longitudinal train simulations (LTS) and multiphysics vehicle multibody simulations (MBS) to determine rail damage. These rail damage results are then used to train a machine learning algorithm surrogate model to predict rail damage using inputs available onboard the locomotive. The proposed framework generates rail damage predictions in real time, without the need of rerunning complex simulations for different operational scenarios. © ASCE.

关键词： Digital elevation model

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Neural Contextual Adaptation (NCA): For Unsupervised Learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

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Position Estimation for Mobile Robots using Machine Learning 3

Position Estimation for Mobile Robots using Machine Learning

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Varma, Kallepalli Rahul Sreenivasan, Medha Srivani, N. Bhattacharya, Shourjyo Glace Varghese, T. Kochuvila, Sreeja Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Robot localization is a critical challenge present in autonomous robots to estimate their position within the environment, ensuring accurate navigation and task execution. This research project compares the position estimation capabilities of advanced machine learning methods Gradient Boosting and K-Nearest Neighbors. In this work Machine Learning (ML) models are built using a dataset from *** that tracked Received Signal Strength Indicator (RSSI) readings and tested them against strong performance measurements. This work findings show GB provides better results than KNN because it reaches an R2 score of 0.9341. These insights are applicable in various fields, including autonomous vehicles, industrial automation, service robotics, and multi-robot coordination, guiding the selection of appropriate algorithms for specific localization challenges. © 2025 IEEE.

关键词： Gradient Boosting K-Nearest Neighbors Machine learning Navigation Robot localization

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