针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器...针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器人专为提升清洁效率和自动化水平设计,配备了远程控制、视觉识别、智能抓取、重量检测及状态显示等功能。采用英伟达Jetson Nano作为核心处理器,结合Intel D415深度相机和基于FloW数据集训练的YOLOv8算法,实现水面漂浮垃圾的实时检测与精确定位。系统通过STM32微控制器解析视觉数据并控制机械臂完成精准抓取。为提高移动性能,机器人采用麦克纳姆轮实现全向运动,当内置称重传感器检测到收集装置满载时,系统可自主返回基地卸载垃圾。此外,系统集成HC-05蓝牙模块实现远程无线控制,并通过OLED显示屏实时显示工作状态。综合应用了AIoT、自动化控制及视觉识别技术,突破了传统清洁方式的局限,显著提升了沙滩水域清洁工作的效率和便捷性,为环保行动提供了强有力的工具。展开更多
With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability ...With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability for power flow calculations to support unit dispatch.Based on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce model.This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data.Experiments are conducted on Spark cluster which is deployed as a cloud computing platform.They show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale calculations.In addition, running time will be reduced when adding cluster nodes.Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.展开更多
文摘针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器人专为提升清洁效率和自动化水平设计,配备了远程控制、视觉识别、智能抓取、重量检测及状态显示等功能。采用英伟达Jetson Nano作为核心处理器,结合Intel D415深度相机和基于FloW数据集训练的YOLOv8算法,实现水面漂浮垃圾的实时检测与精确定位。系统通过STM32微控制器解析视觉数据并控制机械臂完成精准抓取。为提高移动性能,机器人采用麦克纳姆轮实现全向运动,当内置称重传感器检测到收集装置满载时,系统可自主返回基地卸载垃圾。此外,系统集成HC-05蓝牙模块实现远程无线控制,并通过OLED显示屏实时显示工作状态。综合应用了AIoT、自动化控制及视觉识别技术,突破了传统清洁方式的局限,显著提升了沙滩水域清洁工作的效率和便捷性,为环保行动提供了强有力的工具。
基金supported by National Natural Science Foundation of China (No.51677072)
文摘With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability for power flow calculations to support unit dispatch.Based on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce model.This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data.Experiments are conducted on Spark cluster which is deployed as a cloud computing platform.They show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale calculations.In addition, running time will be reduced when adding cluster nodes.Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.
