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共 2742 条记录，以下是31-40 订阅

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Design of Farmland Meteorological Visualization Management System Based on Digital Twins

Design of Farmland Meteorological Visualization Management S...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Xu, Yong Lv, Zhenqiang Chen, Zhiling Chan, Choon Kit Luo, Kun School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia Inti International University Faculty of Education and Liberal Arts Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

To precisely monitor farmland conditions, we need to mitigate the risk of crop yield reduction due to natural disasters and other factors, and enhance the accuracy of agricultural production decisions, an agricultural meteorological environment monitoring system based on digital twin technology has been developed. Unlike traditional meteorological monitoring systems, this design employs digital twin technology to construct a twin model that virtually replicates the physical farmland environment. Meteorological monitoring equipment collects farmland data, which is then uploaded to a virtual visualization interface where real-time remote monitoring, anomaly alerts, and weather forecasts are provided. Experimental results indicate that this system effectively collects and uploads farmland data, enables real-time remote monitoring, and issues timely alerts for abnormal meteorological conditions, thereby offering theoretical guidance and technical support for the advancement of smart agriculture. © 2025 ieee.

关键词： data collection Digital twin visualization weather forecast weather monitoring

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Qin, Manping Xu, Yong Chan, Choon Kit Chen, Zhiling Zhu, Caitong School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring. © 2025 ieee.

关键词： Fetal monitoring

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Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation 25th

Robotic Precision Fitness: Accurate Pose Training for Elderl...

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25th international conference on Intelligent Data Engineering and Automated Learning

作者： Rincon, J. A. Marco-Detchart, C. Univ Burgos Dept Digitalizac Escuela Politen Super Ctra Oron 28 Burgos 09200 Spain Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

This study presents a novel robotic assistant tailored for elderly rehabilitation. The system combines state-of-the-art artificial intelligence, computer vision, and robotics to provide customized exercise routines and instant feedback. The core of the assistant is a Grove AI V2 vision system, driven by a dual-core Arm Cortex-M55 processor and an Arm Ethos-U55 neural network component. A custom-built robotic torso fitted with 12 servo motors supports the demonstration of a wide variety of upper body exercises. A crucial element in the experiments is the use of Restricted Radial Equivalence Functions (RREFs) and Radial Similarity Measures (RSMs) to precisely match human poses with the robot's demonstrations. This method ensures an accurate evaluation of exercise performance, enhancing the efficacy of physical therapy for the elderly. Using edge computing technologies, the system provides real-time monitoring and personalised care while addressing important privacy issues. The assistant is additionally characterized by a small-sized design that includes a 124x64 pixel OLED display, capable of displaying bio-signal monitoring and an animated face to engage users. This research advances AI-assisted elderly care, aiming to increase independence, physical health, and overall quality of life for aging populations while offering support to healthcare systems and caregivers.

关键词： Edge computing Elderly Rehabilitation Pose Estimation Robotic Assistance

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SPT: Sequence Prompt Transformer for Interactive Image Segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 ieee.

关键词： computer vision interactive image segmentation

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Role-Specific Reward Design with Large Language Model for StarCraft II

Role-Specific Reward Design with Large Language Model for St...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Sijia Luo, Haonan Zhang, Xu Zeng, Xin Shen, Zhixuan Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350368741

Reward acts as a signal to guide the agent's learning process in Reinforcement Learning (RL), evaluating and assigning rewards to the agent's actions based on their alignment with goals. Designing reward is challenging in multi-agent environment such as StarCraft II benchmark since agents face credit allocation and role adaptation problems. Recent studies have successfully exploited the language understanding and reasoning capabilities of large language models (LLMs) to learn manipulation tasks. Impressed by the remarkable power of LLMs, this paper employs LLMs as role-specific reward designer for playing StarCraft II, making rewards more flexible and task-oriented. Firstly, we develop an interactive text and multi-agent RL environment to study real-time strategy generation in StarCraft II. Secondly, we use LLMs to interpret the game situation and understand agent roles from user instructions. Then, by assigning appropriate subtasks, LLMs quantify the completion of these subtasks to generate role-specific rewards. Further, credit assignment problem is addressed by introducing dynamic reward weights in value decomposition method. In StarCraft II maps, experiments show that role-aligned RL agents trained with our framework achieve superior policy performance, and win rate results demonstrates the effectiveness of our approach in decision-making for micromanagement and long-term planning. © 2025 ieee.

关键词： Large Language Models Multi-agent Reinforcement Learning Reward design StarCraft II Value decomposition

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Advancing In-Situ Bioprinting Through Ant Colony Optimisation: Resolving Dead Zone Challenges in Path Planning 2nd

Advancing In-Situ Bioprinting Through Ant Colony Optimisatio...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Liu, Keyu Huang, Long Feng, Zhiming Ren, Xianlin Chen, Yi School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou China Department of Chemical Engineering University of Manchester Manchester United Kingdom School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819639489

Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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CS-UNet: A LightWeight UNet Model Based On Context Information 17th

CS-UNet: A LightWeight UNet Model Based On Context Informati...

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17th international conference on Intelligent robotics and Applications, ICIRA 2024

作者： Shen, Chenxin Fang, Yinfeng Yu, Xixia Guo, Chunsheng Ju, Zhaojie Pinghu Digital Technology Innovation Research Institute Hangzhou Dianzi University Zhejiang Pinghu China School of Communication Engineering Hangzhou Dianzi University Zhejiang Hangzhou China Department of Internal Medicine Xinhua Hospital of Zhejiang Province The Second Affiliated Hospital Zhejiang Chinese Medical University Zhejiang Hangzhou310003 China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

ISBN: (纸本)9789819607761

Since the introduction of UNet in 2015,U-shaped architecture has become an important paradigm in the field of medical image segmentation. However, due to the inherent local limitations of convolutions, a fully convolutional segmentation networks with U-shaped architecture cannot effectively utilize global context information. Although the combination of transformers and CNNs can address these issues, it also brings larger computational cost. At the same time, Due to the specificity of medical scene, scarce medical resources will be an obstacle to the application of transformers in the medical field, which is well adapted by the inductive bias in lightweight networks. For this reason we propose CS-UNet, a lightweight fully convolutional model that enables fast image segmentation in real medical scenarios. In this model, we reconstruct the encoder and decoder to generate a dense receptive domain to extract contextual information. We evaluate CS-UNet on five different types of medical image datasets, and found that CS-UNet excelled in segmentation performance. While ensuring high accuracy, CS-UNet has faster computation speed and smaller model parameters, which achieves a better balance between performance and computation cost, and is less constrained by the environment as well as the equipment in practical application scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Synergizing Underwater robotics and AI: A Tethered ROV and YOLOv7 Approach to Giant Clam Detection 2nd

Synergizing Underwater Robotics and AI: A Tethered ROV and Y...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Badhan, Al Mominul Haque Hanif, Mariam Faudzi, Ahmad Athif Mohd Hafidz, Muhamad Hazwan Abdul Ruslan, Mohammad Izzuddin Bin Adam, Muhammad Amirul Amri Khamis, Nurulaqilla Faculty of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia A2TECH Sdn. Bhd Johor Bahru81310 Malaysia

ISBN: (纸本)9789819639489

Integrating a tethered Remotely Operated Vehicle (ROV) with the YOLOv7 object recognition algorithm offers a novel approach to marine monitoring, particularly for tracking giant clam populations in the reefs of Tioman Island, Malaysia. The ROV, designed for enhanced underwater exploration, is equipped with advanced imaging and sonar technologies, resilient tethered power management and communication systems. The control and navigation system are built on the pixhawk flight controller, supported by ArduPilot firmware and a customized QGroundControl interface, ensuring real-time operations. The primary objective of this research is to develop an algorithm capable of accurately recognizing and counting giant clams through underwater images and videos. The study compares the performance of the YOLOv7 algorithm with the manual counting. The findings indicate that the YOLOv7 algorithm demonstrates average accuracy of the model of 79.65%. The integration of YOLOv7 enables identification and quantification of giant clam populations enhancing data collection for research and conservation efforts providing practical solutions for environmental monitoring and the sustainable management of marine resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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Edge AI to Edge robotics: Enhancing Human Pose Estimation with High-Performance TPU computing 4th

Edge AI to Edge Robotics: Enhancing Human Pose Estimation wi...

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Sharma, Vijeta Mupenda, Didier Numbi Thorvik, Lars Mishra, Deepti NTNU-Norwegian University of Science and Technology Gjøvik2815 Norway

ISBN: (纸本)9783031837821

The convergence of Edge AI and high-performance computing (HPC) is revolutionising intelligent systems by enabling real-time data processing and decision-making at the network’s edge. The demand for high-performance computing in resource-constrained environments opens opportunities for applications such as autonomous vehicles, smart cities, and industrial automation. This study presents the terminology that evidences the evolution of edge AI to the edge robotics landscape, and then we present the novel approach to leveraging Tensor Processing Units (TPUs) to enhance the inference capabilities of robotic systems at the edge. Using a Reachy robot as a case study, we demonstrate real-time human pose estimation powered by TPUs. The research investigates the intersection of robotics, Edge AI, and HPC, focusing on accelerating inference tasks with TPUs. We implemented deep learning-based human pose estimation models—PoseNet-ResNet50, PoseNet-MobileNet V1, MoveNet Lightning, and MoveNet Thunder—on the Robot Operating System (ROS) and benchmarked their performance. Results show that MoveNet Thunder achieved the fastest inference time (48 ms) with a pose score of 98%, while PoseNet-ResNet50 was the slowest (701 ms) with a score of 93%. Our findings highlight the significant improvements in inference speed and accuracy using TPUs over CPUs. We discuss the implications of TPU-accelerated AI in robotics and explore future research directions in this evolving field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tensors

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Big Data Analytics in Industry 4.0: Automating Production Metrics With an Edge-Based Architecture

Big Data Analytics in Industry 4.0: Automating Production Me...

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ieee international conference on Industrial Technology (ICIT)

作者： Arthur Kretzer Denise Costa Gabriel Pardini Jardel Wolkers Renato Luz Frank Siqueira Cooperated Production Center - CPC CERTI Foundation / UFSC Florianópolis Brazil Research and Development - R&D Lenovo Indaiatuba Brazil Department of Informatics and Statistics - INE Federal University of Santa Catarina - UFSC Florianópolis Brazil

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents the development and evaluation of a Big Data Analytics (BDA) platform designed for Industry 4.0 applications, focusing on the integration, processing, and visualization of data at the edge. The platform integrates tools such as Apache Spark, Airflow, MinIO, Airbyte, and Streamlit, to facilitate automated data collection and report generation. Deployed in a real manufacturing environment in São Paulo, Brazil, the solution aims to minimize data latency by avoiding cloud transmission and leveraging edge computing. Automated daily reports have been generated since May 2024, improving decision-making and reducing human error associated with manual data integration. This work provides a practical contribution to industrial data integration, highlighting the benefits of edge-based data processing, automation, and visualization to optimize production efficiency and decision-making processes.

关键词： Measurement Decision making Data visualization Data integration Production Manuals Big Data real-time systems Fourth Industrial Revolution Edge computing

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