Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neura...
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ISBN:
(数字)9798350356755
ISBN:
(纸本)9798350356762
Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.
Recent advancements in Large Language models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve ef...
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ISBN:
(数字)9798331509231
ISBN:
(纸本)9798331509248
Recent advancements in Large Language models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve efficient collaboration and decision-making in dynamic and uncertain environments, which are common in real-world multi-robot scenarios. To address these challenges, we propose a novel retrospective actor-critic framework for multi-robot collaboration. This framework integrates two key components: (1) an actor that performs real-time decision-making based on observations and task directives, and (2) a critic that retrospectively evaluates the outcomes to provide feedback for continuous refinement, such that the proposed framework can adapt effectively to dynamic conditions. Extensive experiments conducted in simulated environments validate the effectiveness of our approach, demonstrating significant improvements in task performance and adaptability. This work offers a robust solution to persistent challenges in robotic collaboration.
In this paper, we present an automated integration approach for BIM and 3D GIS data about infrastructure projects to improve human-machine interaction with respect to sustainable project management. Based on the step-...
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Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machin...
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robotics testing is a vital process for ensuring the safety, reliability, and robustness of autonomous systems deployed in complex environments. As robots increasingly interact with dynamic and unpredictable environme...
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ISBN:
(数字)9798331533816
ISBN:
(纸本)9798331533823
robotics testing is a vital process for ensuring the safety, reliability, and robustness of autonomous systems deployed in complex environments. As robots increasingly interact with dynamic and unpredictable environments, rigorous testing is essential to prevent operational failures. Despite significant advancements in robotic systems, existing methodologies often fail to meet the demands of diverse and evolving scenarios. This paper introduces a novel framework that integrates adaptive testing strategies, scalable infrastructures, and AI-driven fault analysis to address these challenges. The framework employs cloudbased simulation testing, real-world validation, and machine learning models for fault detection and prediction. Case studies on autonomous drones and warehouse robots demonstrate its effectiveness in improving test coverage, fault detection rates, and operational reliability. Comparative analyses highlight the superiority of this framework over existing approaches. The proposed solutions address critical gaps and provide a path toward standardized robotics testing practices.
This paper discusses the advantages and disadvantages of robots as medical devices for clinical applications. Since the 1980s, robots have been used in vivo (in vivo refers to use related to medical purposes). Unlike ...
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ISBN:
(数字)9798350356755
ISBN:
(纸本)9798350356762
This paper discusses the advantages and disadvantages of robots as medical devices for clinical applications. Since the 1980s, robots have been used in vivo (in vivo refers to use related to medical purposes). Unlike robots in other industries, surgical robots are held to strict standards and, like other medical devices, must be precisely and effectively tuned. This paper provides a comprehensive survey of current advances in the field and identifies some of the current challenges faced by the field. In such systems, it is extremely important to maximize the effectiveness of medical procedures, addressing the limitations of human capabilities. Various models of medical robots have been put into clinical and practical applications, including microbots, capsule cameras, and da Vinci surgical robots. However, these robots still require human operation to complete medical tasks. More autonomous medical robots are currently under development. It is believed that in the future, more precise, effective, safe, and autonomous medical robots will be developed and put into use.
Mobile robots are becoming increasingly ubiquitous in modern society, requiring more human-like interaction capabilities, such as following operator instructions and collaborating with humans. Conventional robot progr...
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ISBN:
(数字)9798331516857
ISBN:
(纸本)9798331516864
Mobile robots are becoming increasingly ubiquitous in modern society, requiring more human-like interaction capabilities, such as following operator instructions and collaborating with humans. Conventional robot programming methods often fall short in achieving these complex behaviors. Behavior Trees (BTs) offer a promising alternative due to their modularity, scalability and reactivity. We propose utilizing general purpose, system-prompted Large Language Model (LLM) assistants to decompose task descriptions into executable BTs, which are subsequently refined using Genetic Programming (GP) and a state machine like low-resource BT execution simulator, where they will be tested for task completion. This approach eliminates the need for fine-tuning LLMs, thereby reducing computational costs and saving time and energy. Our method successfully solves all proposed scenarios, enhances applicability across diverse environments, and democratizes behavior generation for non-experts, outperforming baseline methods in efficiency.
Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token desig...
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Parkinson's disease (PD) is a neurodegenerative disorder that needs early and accurate diagnosis for good management. Our approach in this study is to have a hybrid diagnostic framework which uses Convolutional Ne...
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ISBN:
(纸本)9798331542375
Parkinson's disease (PD) is a neurodegenerative disorder that needs early and accurate diagnosis for good management. Our approach in this study is to have a hybrid diagnostic framework which uses Convolutional Neural Networks (CNNs) for the feature extraction, and Support Vector Machines (SVMs) for the classification. By leveraging on the complementary strengths of the above-mentioned approaches, the model reports improved performance in detection of PD by using medical imaging data. We evaluated proposed CNN-SVM hybrid model on a dataset consist of 5,000 MRI images (2,500 with PD cases and 2,500 with healthy control) with achieved accuracy of 94.8%, sensitivity of 93.7%, and specificity of 95.5%. These results demonstrate a significant improvement over baseline methods, including standalone CNNs (accuracy: 91. The performance of the proposed methods is experimentally demonstrated to be better than SVMs with handcrafted features (accuracy: 84.5%, sensitivity: 83.2%, specificity: 85.0%) and even SVMs using Fisher vectors (accuracy: 81.4%, sensitivity: 86.1%, specificity: 81.1%) for certain classes (e.g. 2%, sensitivity: 89.5%, specificity: 92.3%). The class separability of the hybrid model was excellent, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.96. These findings were further confirmed under confusion matrix analysis with 2,343 true positives and 2,388 true negatives and with only 157 false negatives (sensitivity: 0.017%) and 112 false positives (specificity: 0.970%). The model demonstrated generalizability by the convergence of training and validation curves throughout 50 epochs with a final loss of 0.06 and validation error stabilizing at 0.04. The CNN-SVM hybrid model also can demonstrate the ability to become a reliable diagnostic tool for PD detection, with robust classification performance and few errors. Multi-modal data integration and scalability for use over a broader range of clinical applications is the subject of fut
The proceedings contain 21 papers. The special focus in this conference is on Web-Based Learning. The topics include: Towards Effective Collaborative Learning in Edu-Metaverse: A Study on Learners’ Anxiety,...
ISBN:
(纸本)9789819644063
The proceedings contain 21 papers. The special focus in this conference is on Web-Based Learning. The topics include: Towards Effective Collaborative Learning in Edu-Metaverse: A Study on Learners’ Anxiety, Perception, and Behaviour;an Empirical Study of Code Pattern Reuse and Pattern Preference Consistency in Novice Programmers;self-attentive Knowledge Tracing with Relative Position Encoding;students’ Pre-concepts of Robot Control Before Their First robotics Lesson: A Pilot Study;evaluating Verbs of Chatting in Large Language models;mapping the Landscape of Digital and Intelligent Technologies in Intercultural Learning: A Bibliometric Analysis (2014–2024);AI-Enhanced Intercultural Teaching: Investigating Chinese EFL University Teachers’ Technology Readiness and Acceptance of Large Language models;leveraging Data Visualization with ggplot2 in Translation Pedagogy: Enhancing Learning Through Visual Insights;AI-Assisted Writing on News Report: A Case Study on AI-Generated Content and Human-Generated Content;Analysis of Interpreter Performance on Numbers, Proper Nouns, and Complex Sentences in AI-Assisted English-Chinese Simultaneous Interpreting: An Eye-Tracking Experimental Study;Human v.s. AI: A Comparative Study Between MTI Student Translators and AI Engines;knowledge Graph-Based Advance Preparation in Medical Interpreting;Quality Comparison of MT in Translating Chinese Music Historical Texts and Inspiration for Translation Pedagogy;Human-in-the-Loop: A Conceptual Framework for Business English Teachers’ AI-Empowered Assessment;an Exploratory Research on Reading Comprehension Question Generation Based on Annotated Clause Complex Corpus;ChatGPT Prompt Engineering methods for Financial News Translation;A Robust and Secure Framework for an AI-Assisted Academic Writing Platform;an Automatic Model for Lesson Plans Generation Based on Logical Chains and Prompt Tuning.
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