Research on simulation of gun muzzle flow field empowered by artificial intelligence

Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.

Research and Application of Artificial Intelligence in Electric Guitar Performance

The article explores the application of artificial intelligence technology in electric guitar playing.It delves into the advantages of artificial intelligence and its seamless integration into electric guitar performance.Additionally,it investigates the application of artificial intelligence technology through an intelligent playing robot.The research aims to offer substantial support for the advancement of artificial intelligence in electric guitar performance.

Past,present,and future perspectives of ultrasound-guided ablation of liver tumors:Where could artificial intelligence lead interventional oncology?

The first ablation procedures for small hepatocellular carcinomas were percutaneous ethanol injection under ultrasound(US)guidance.Later,radiofrequency ablation was shown to achieve larger coagulation areas than percutaneous ethanol injection and became the most used ablation technique worldwide.In the past decade,microwave ablation systems have achieved larger ablation areas than radiofrequency ablation,suggesting that the 3-cm barrier could be broken in the treatment of liver tumors.Likewise,US techniques to guide percutaneous ablation have seen important progress.Contrast-enhanced US(CEUS)can define and target the tumor better than US and can assess the size of the ablation area after the procedure,which allows immediate retreatment of the residual tumor foci.Furthermore,fusion imaging fuses real-time US images with computed tomography or magnetic resonance imaging with significant improvements in detecting and targeting lesions with low conspicuity on CEUS.Recently,software powered by artificial intelligence has been developed to allow three-dimensional segmentation and reconstruction of the anatomical structures,aiding in procedure planning,assessing ablation completeness,and targeting the residual viable foci with greater precision than CEUS.Hopefully,this could lead to the ablation of tumors up to 5-7 cm in size.

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

基于Community Intelligence的水电机组融合监测方法

介绍了基于CIA(Community Intelligence Agent)模型的水电机组融合监测框架:CIA模型包含消息处理层、行为控制层及核心功能层3个层次,各CIA与外界(如其他CIA)通过Request消息或Report消息联系;融合监测框架由维护域CIA系统和控制域CIA系统构成,维护域CIA系统各CIA之间通过实时现场总线(如CAN总线)或工业以太网实现交互、协作、关联动作及数据共享。将整个机组的状态数据划分为原始采样数据、瞬变状态数据、详细状态数据及概要状态数据4个层次,并针对不同层次的状态数据,进行选择性智能存储,以提高机组运动状态分析和故障诊断的针对性及有效性,节约存储空间。通过时间融合、运行工况融合、异常事件融合和冲突解决策略,实现融合监测系统中各CIA之间实时交互、协作及关联动作和机组状态数据的融合,有效地保证来自机组不同子系统的运动状态的关联性。

Cooperative Intelligence for Autonomous Driving

Autonomous driving is an emerging technology attracting interests from various sectors in recent years.Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules.In this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques.We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles.Following this,a cooperative intelligence framework is proposed for autonomous driving systems.This general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Vision‐audio fusion SLAM in dynamic environments

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment perception.To adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles.However,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots.In the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is difficult.However,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more difficult.To address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent systems.Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

面向智能制造仿真实验的产线数字孪生建模

针对智能制造领域人才培养中,实践教学存在的成本高、安全保证难等问题,该研究构建了智能制造仿真实验系统。首先,以智能产线为基础、车间信息化平台为桥梁,融合产线数字孪生技术,构建了面向智能制造仿真实验的数字孪生系统框架;其次,对系统中包含的产线物理实体模型、数据采集及车间信息化平台、数字孪生产线的多维度模型及智能制造仿真实验平台展开了详细阐述;最后设计产线生产调度优化实验,验证了该数字孪生仿真实验系统的有效性。

面向机器智能的情感分析

大模型的兴起,尤其是以大模型为基础的人工智能技术的发展与应用引起了全社会对人工智能技术的广泛关注。人工智能技术已经在很多方面达到甚至超越人类,可以替代人工完成一些技艺要求较高的工作。不过,在人工智能迈向人类智慧的道路上,仍然有许多难题亟待克服与解决,其中一个重要的问题就是情感分析与理解问题,如让机器准确理解人类的情感。针对该问题,文章从情感的定义与分类入手,对情感分析研究需要解决的问题,研究方向、研究现状、面临的挑战、应用前景与展望等进行了讨论。

数字孪生在制造业中实现的关键技术及典型应用综述

数字孪生技术是实现智能制造和工业数字化转型的关键技术,受到学术界和产业界的广泛关注和研究。近十年来,随着数据采集技术、计算机性能和相关智能算法的迅速发展,数字孪生技术的研究及应用取得了巨大进展。针对数字孪生技术应用领域的多样性和对象层级的复杂性,本文回顾了数字孪生基本概念和模型的发展历程,综述了数字孪生技术在制造业应用的关键技术及其发展成果。在此基础上,阐述了数字孪生技术在制造业三个层级的应用现状以及数字孪生赋能智能制造的新范式。最后,展望了制造业数字孪生技术未来发展趋势,为后续研究提供参考。

智能人机交互在有人太空探索中的应用与展望

近年来,空间机器人技术发展迅速,国内外已有诸多空间站机器人、深空探测机器人的应用实例。未来有人太空探索过程当中,人与众多智能机器人协同开展任务将成为必然趋势。对国内外空间协作机器人应用实例进行了总结,在分析人机协作机制的基础上,提出了空间人机交互的关键技术,包含人体检测与跟踪、手势识别与分类、动作识别与意图预测、低重力环境下的人机协同控制等,对上述关键技术的难点与研究进展进行了分析综述,并对后续的发展方向进行了展望。

高校实验室安全风险监控融合预警与数字化决策系统

高校实验室风险点与危险源密集,人机环管因素持续变化,且人员流动快、前沿探索多,导致风险隐患动态识别难、预判预警精准度不高、风险管控精细化水平不足。围绕实验室安全风险精准辨识与快速响应这一关键问题,基于实验室安全风险—隐患—事故演化规律,建立安全风险监测监控指标体系,运用物联网、人工智能、大数据分析等技术,开发安全隐患视频图像智能辨识算法,研发多源信息感知融合分析技术,研究应急响应辅助决策方法,从而构建“云边端”一体化的融合监控预警与数字化决策系统,并在学校安全工程学院实验室应用。实践表明,该系统能够对实验室进行全时间—空间—要素的多维度持续覆盖,及时发现风险隐患并有效应急响应,有助于提升实验室安全风险管控与应急能力,具有一定推广应用价值。

人工智能技术赋能我国档案工作发展的现状与建议

为了解当前我国档案领域人工智能技术应用的发展现状,论文运用内容分析法对全国及各省档案事业“十四五”规划、档案领域人工智能应用实践案例及国家档案局科技项目立项进行分析。研究发现我国鼓励并支持人工智能技术在档案领域的应用,正在初步探索人工智能助推档案工作的路径和方法。在此基础上,论文从三个方面提出建议,分别是:推动人工智能技术在档案全流程管理中的应用,推动人工智能技术与档案工作的深度融合,以及关注人工智能技术成功应用于档案工作的影响因素。

面向数字孪生变压器的计算机辅助运维(CAM+)构架与关键技术

发展数字孪生变压器对支撑电力装备智能运维具有重要价值。目前变压器已具有计算机辅助设计、工程仿真的一般功能,但面向运行维护的场景、性能数字孪生还处于初级阶段。为此,该文提出了一种将计算机辅助设计、工程仿真、制造进一步拓展为电力装备的计算机辅助运维(computer aided maintenance+,CAM+)新模式,包含了场景更新、虚实融合、故障定位、智能运维等功能。该文首先介绍了CAM+的内涵,结合电力装备的数字孪生架构提出了面向数字孪生变压器的CAM+模式的技术架构。然后,综述了3维场景可视化建模、高精度传感、多物理场耦合模型与快速计算、虚实融合的动态推演、多源数据融合、状态智能评估、知识图谱等CAM+模式所涉及的关键技术。最后,分析了CAM+在变压器场景更新、参量孪生和智能运维的应用,并总结了未来CAM+所面临的挑战和发展方向。

具身传播视角下人工智能在档案信息传播中的应用研究

人工智能技术有助于将具身体验与信息传递过程深度融合,使得传播生态从内涵到外延都发生了很大的变化。文章主要从传播学角度出发,深入探讨了具身传播趋势下,档案信息传播向具身化转变的实践中人工智能所提供的技术可行性,详细分析了人工智能技术为档案信息传播带来的实现交互叙事、提升视角体验、跨越时空边界、创新传播模式等机遇,以及所面临的影响内容呈现、削弱传播效果等挑战,思考人工智能技术在档案信息传播中风险监管、内容挖掘与技术利用三个方面的创新实践,以期为我国档案信息传播工作的创新与发展提供一定的参考。

基于改进樽海鞘群算法的含瓦斯煤破裂过程信号特征识别

针对标准樽海鞘群算法存在的计算精度不足、易陷入局部停滞等缺陷,提出一种多策略融合的樽海鞘群算法。在初始化阶段,引入线性同余法随机发生器;利用野马算法优化樽海鞘领导者位置;采用金豺算法改进樽海鞘种群追随机制。通过测试函数寻优对比实验,证明多策略融合的樽海鞘群算法相比于其他智能算法在鲁棒性与稳定性方面均有显著提升。将多策略融合的樽海鞘群算法应用到含瓦斯煤破裂过程信号特征识别,实验结果表明:提出的含瓦斯煤破裂过程信号特征识别模型具有更好的表现,准确率可达93.33%,相比其他识别模型,识别率更高。

基于人工智能技术的机器人运动控制系统设计

设计一种基于人工智能技术的机器人运动控制系统,确保机器人更好地理解人类的意图,并提供更加人性化的服务。该系统通过运动数据采集与传输组件连接机器人的轴电机,采集机器人当前运动数据后,将其传输到控制器组件内,控制器组件依托X86架构工控机,使用PIC总线将采集到的机器人当前运动数据发送到基于人工智能技术的机器人运动路径规划模块内。该模块运用人工智能技术中的A*算法获取机器人轨迹路径规划结果后,依据该路径规划结果,将人工智能技术中的神经网络和模糊B样条基函数相结合,建立模糊B样条基函数神经网络控制器。该控制器输出机器人运动控制指令,并发送给伺服驱动器组件,伺服驱动器负责驱动机器人轴电机,控制机器人运动。实验结果表明:所设计系统具备较强的机器人路径规划能力,可在复杂路径情况下实现机器人运动控制,且控制精度和控制阶跃响应能力均较强。

基于多模态数据融合与机器学习的高价值专利早期识别方法

[研究目的]如何构建有效的高价值专利识别方法,从海量的专利中识别出高价值专利,对于高价值专利的培育、运用、保护和管理具有重要意义。[研究方法]提出一种基于多模态数据融合与机器学习的高价值专利早期识别方法。首先,利用多模态融合方法对专利指标数据和文本数据两种模态数据进行融合;其次,利用基于机器学习的生存预测算法对历史已知的高价值专利进行分析,并获取高价值专利融合后的特征与其“高价值”之间的关联模式;最后,利用获取到的关联模式,对新公开专利的“价值”进行预判,从而实现对高价值专利的早期识别。[研究结论]以人工智能领域专利为例进行实证研究,验证了该方法的可行性和有效性,为高价值专利早期识别提供了新的研究方法。