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基于文本分类技术的智能机器学习文字自动识别
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作者 罗丹 《周口师范学院学报》 CAS 2020年第5期66-69,共4页
针对传统的智能机器学习文字自动识别方法存在的识别率较低的问题,开展了基于文本分类技术的智能机器学习文字自动识别方法的设计.首先结合文字自动识别流程,采用建设网络蜘蛛爬虫的方式过滤文本特征值,实现基于文本特征值的文字自动分... 针对传统的智能机器学习文字自动识别方法存在的识别率较低的问题,开展了基于文本分类技术的智能机器学习文字自动识别方法的设计.首先结合文字自动识别流程,采用建设网络蜘蛛爬虫的方式过滤文本特征值,实现基于文本特征值的文字自动分类.其次,采用分层处理的方式进行文字的自动提取.同时建立特征化文本数据库,为多元化文字提供存储方式,实现文字特征量的自动识别.最后通过设计实验的方式验证,设计的方法比传统方法对文字的识别率要高,为文字识别领域的发展提供了发展方向. 展开更多
关键词 文本分类技术 智能机器学习 文字自动识别
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机器学习在石油工业中的应用:地球科学·油藏工程·生产工程
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作者 王宏琳 《石油工业计算机应用》 2023年第1期73-94,2,共23页
人工智能/机器学习(AI/ML)已经成为大数据、机器人和物联网等新兴技术的主要驱动力。数字化转型深入到石油和天然气行业,以重塑地球科学、油藏工程和生产工程,寻求勘探和生产(E&P)业务更高的生产率。AI/ML是即将到来的下一个技术突... 人工智能/机器学习(AI/ML)已经成为大数据、机器人和物联网等新兴技术的主要驱动力。数字化转型深入到石油和天然气行业,以重塑地球科学、油藏工程和生产工程,寻求勘探和生产(E&P)业务更高的生产率。AI/ML是即将到来的下一个技术突破。通过在石油和天然气运营中利用AI/ML,可以设计算法来指导E&P。AI/ML系统将使用E&P作业的历史数据进行训练。 展开更多
关键词 石油工业 勘探与生产 人工智能/机器学习 地球科学 油藏工程 生产工程 数字孪生体 物联网
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智能建筑结构施工中的数字化管理与控制技术创新实践
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作者 杨凡 《中文科技期刊数据库(全文版)工程技术》 2024年第10期0050-0053,共4页
随着智能建筑的快速发展和普及,对建筑结构施工的数字化管理与控制技术需求日益明晰和迫切。本研究主要关注了在智能建筑结构施工中的数字化管理与控制技术的探讨和研究。研究首先对智能建筑的概念进行了解读,接着阐述了数字化管理与控... 随着智能建筑的快速发展和普及,对建筑结构施工的数字化管理与控制技术需求日益明晰和迫切。本研究主要关注了在智能建筑结构施工中的数字化管理与控制技术的探讨和研究。研究首先对智能建筑的概念进行了解读,接着阐述了数字化管理与控制技术的重要性,并结合实际案例,研究了各种数字化管理与控制工具和策略在实际施工中的应用情况和效果。研究结果表明,数字化管理与控制技术能够在施工过程中提升效率、降低成本、减少错误和加强质量控制。尤其是通过实时数据收集和分析、自动化建模和优化设计、以及人工智能与机器学习等先进技术的引入,使得施工管理可视化、精确和智能化水平得到了极大的提升。进一步研究和改良这些管理与控制技术,对于我国智能建筑行业的持续健康发展有着十分重要的实践意义。 展开更多
关键词 智能建筑 数字化管理 控制技术 效率提升 人工智能机器学习
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电梯维护管理中的智能化技术应用研究
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作者 宋其全 陈袁园 《中文科技期刊数据库(引文版)工程技术》 2024年第1期0195-0198,共4页
电梯维护管理的重要性和现存问题是目前高度关注的议题。本文首先分析了电梯维护管理的重要性,指出存在的主要困难与挑战,并深入探讨了电梯维护管理中存在的主要问题和改进空间。随后,通过研究,提出了将智能化技术应用于电梯维护管理,... 电梯维护管理的重要性和现存问题是目前高度关注的议题。本文首先分析了电梯维护管理的重要性,指出存在的主要困难与挑战,并深入探讨了电梯维护管理中存在的主要问题和改进空间。随后,通过研究,提出了将智能化技术应用于电梯维护管理,如云计算与大数据、物联网技术以及人工智能和机器学习技术的应用潜力和前景。在此基础上,针对电梯维护管理,本文研究并提出了基于大数据的电梯故障预测和预防策略,基于物联网的电梯实时监控和远程诊断策略,以及基于人工智能的电梯故障自动识别和处理策略。此研究的成果不仅有助于提升电梯安全性,也为推动电梯维护管理的智能化提供了有效策略。 展开更多
关键词 电梯维护管理 智能化技术 云计算与大数据 物联网技术 人工智能机器学习
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论智能时代的人—机合作式学习 被引量:42
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作者 王竹立 《电化教育研究》 CSSCI 北大核心 2019年第9期18-25,33,共9页
网络和智能技术的飞速发展,导致人类社会方方面面都发生了翻天覆地的变化,教与学也不能例外。文章深入分析了知识与学习发生的变化,探讨了人类学习与机器学习的异同,以及人—机合作学习的必然性和可能的方式,得出以下几点结论:(1)网络... 网络和智能技术的飞速发展,导致人类社会方方面面都发生了翻天覆地的变化,教与学也不能例外。文章深入分析了知识与学习发生的变化,探讨了人类学习与机器学习的异同,以及人—机合作学习的必然性和可能的方式,得出以下几点结论:(1)网络和智能时代,知识和学习发生了很大变化,出现了软知识、人—机知识等新的知识类型,未来还有可能出现暗知识;(2)知识生产也逐渐由人类主体向人—机共同主体转变;(3)人—机合作式学习将成为主流学习模式,现阶段以人—网合作学习为主,未来将以人—智能机器合作学习为主,教育教学也将变为人—机合作式教学模式;(4)技术在人类学习中扮演的角色由单纯的工具,向环境、伙伴角色转变,并最终可能与人类连成一体。为此,今天教育教学亟需发生重大变革,以适应未来的需要。 展开更多
关键词 软知识 人机知识 暗知识 人-机合作式学习 人-智能机器一体式学习
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ROV水下障碍物检测和避障技术的应用综述
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作者 李明桂 周焕银 龚利文 《计算机工程与应用》 CSCD 北大核心 2024年第17期34-47,共14页
全面回顾了远程操作车(ROV)在水下障碍物检测和避障技术方面的技术进展。研究集中于声呐系统、光学系统及其与机器学习和人工智能算法的结合,分析了这些技术如何提高水下作业的自主性、效率和安全性。尽管声纳和光学系统在环境适应性和... 全面回顾了远程操作车(ROV)在水下障碍物检测和避障技术方面的技术进展。研究集中于声呐系统、光学系统及其与机器学习和人工智能算法的结合,分析了这些技术如何提高水下作业的自主性、效率和安全性。尽管声纳和光学系统在环境适应性和障碍物检测精度方面已取得显著成果,但动态障碍物实时识别和复杂环境适应性的挑战仍待克服。此外,探讨了机器学习和人工智能技术在增强ROV自主避障能力方面的潜力和挑战,指出了这些技术在未来ROV操作中的重要性。该研究为深海探索和海洋科学提供了新的理论视角和应用实践。 展开更多
关键词 水下障碍物检测 自主避障 声纳系统 光学系统 机器学习与人工智能
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基于传感器融合技术的高压开关柜智能运检方案 被引量:5
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作者 韩博文 顾博川 +2 位作者 陈小军 余膺昊 宋旭东 《电气应用》 2020年第9期98-103,共6页
目前开关柜封装为一个整体,柜内的机械、热力以及局部放电等均难以排查,给运维检修带来一定挑战。为此提出了基于传感器融合技术的高压开关柜智能运检方案。首先分析了开关柜的特点以及主要检测物理量的特性。然后分析了开关柜传感器监... 目前开关柜封装为一个整体,柜内的机械、热力以及局部放电等均难以排查,给运维检修带来一定挑战。为此提出了基于传感器融合技术的高压开关柜智能运检方案。首先分析了开关柜的特点以及主要检测物理量的特性。然后分析了开关柜传感器监测需求,提出利用远红外温度传感器、开关操作传感器以及局部放电监测传感器对开关柜的运行进行监测。最后说明了机器学习与人工智能的发展对开关柜运维的影响。 展开更多
关键词 高压开关柜 智能运检 传感器 人工智能机器学习
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网师园“步移景异”时空感知生成机理 被引量:1
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作者 张甜甜 刘滨谊 +1 位作者 朱喆 冯茂桓 《中国园林》 CSCD 北大核心 2023年第11期22-28,共7页
解析中国园林“步移景异”产生形成的感知机制原理,利用网络大数据识别由视点(观赏的立足点)感知而来的“景”及与“景”相应之“异”,对视点及其视觉空间界面予以要素指标量化分析。以苏州古典园林网师园为例,借助网络大数据、调查问... 解析中国园林“步移景异”产生形成的感知机制原理,利用网络大数据识别由视点(观赏的立足点)感知而来的“景”及与“景”相应之“异”,对视点及其视觉空间界面予以要素指标量化分析。以苏州古典园林网师园为例,借助网络大数据、调查问卷精准高效获取所需要素指标数据,利用人工智能机器学习生成决策树,构建模拟步移景异感知生成的模型,得到了4条有利步移景异生成的规则和3条不利步移景异生成的规则。初试结果显示,影响网师园步移景异感知生成指标的贡献程度大小依次为视野中水体占比的变化、景阔变化、景深变化、两视点之间空间转换的次数等。旨在科学量化解析苏州古典园林时空感知的视觉引发生成机理,破解“步移景异”之谜,推进中国古典园林设计的传承弘扬。 展开更多
关键词 风景园林 步移景异 苏州古典园林 景观时空感知 景观视觉感知 网络大数据 人工智能机器学习
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财务危机预警模型研究及其应用
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作者 田军 《当代会计》 2023年第6期118-120,共3页
财务危机是企业面临的严重挑战之一,预警财务危机对企业长期发展至关重要。阐述了财务危机预警模型的概念、分类、发展历程和现状,分析了传统财务比率分析法、多元统计方法、人工智能和机器学习方法以及其他构建财务危机预警模型的方法... 财务危机是企业面临的严重挑战之一,预警财务危机对企业长期发展至关重要。阐述了财务危机预警模型的概念、分类、发展历程和现状,分析了传统财务比率分析法、多元统计方法、人工智能和机器学习方法以及其他构建财务危机预警模型的方法,对财务危机预警模型在风险管理、经营决策支持和投融资决策方面的应用进行了探讨,并提出了现有模型存在的问题和局限性,以及优化方法,基于文献分析和总结,提出了未来财务危机预警模型研究的方向。 展开更多
关键词 财务危机 预警模型 人工智能机器学习方法
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BACKGROUND KNOWLEDGE AND SECONDARY KNOWLEDGE BASES IN LEARNINGS YSTEMS
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作者 王建东 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 1997年第1期9+11+13-14,10+12,共6页
This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background k... This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background knowledge plays an important role in enhancing the ability of a learning system. An explanation based learning system with domain theory in primary knowledge base and background knowledge in secondary knowledge base is introduced as an example. It shows how background knowledge can be used to solve some of the problems caused by incomplete domain theory in an explanation based learning system. The system can accomplish knowledge level learning through purely deductive approach. At last the acquisition of background knowledge is briefly discussed. 展开更多
关键词 artificial intelligence knowledge engineering machine learning background knowledge domain theory
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走向精确地震勘探的道路 被引量:16
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作者 王华忠 盛燊 《石油物探》 CSCD 北大核心 2021年第5期693-708,720,共17页
勘探地震领域已扩展到复杂地表、复杂构造、复杂油藏和深层目标等。地震数据采集技术发展到了宽方位、高密度、宽频带(“两宽一高”)地震数据采集阶段;地震波成像技术发展到了贝叶斯(Bayes)参数估计理论下的全波形反演(FWI)和最小二乘... 勘探地震领域已扩展到复杂地表、复杂构造、复杂油藏和深层目标等。地震数据采集技术发展到了宽方位、高密度、宽频带(“两宽一高”)地震数据采集阶段;地震波成像技术发展到了贝叶斯(Bayes)参数估计理论下的全波形反演(FWI)和最小二乘逆时深度偏移成像(LS_RTM);油藏描述发展到了综合信息利用和最佳判定阶段;地震勘探技术已经发展至全新的阶段。横向缓变的层状介质假设、地表一致性假设、射线理论波传播和Zoeppritz方程界定了上一代精确地震勘探的方法技术及其适用性,上一代精确地震勘探以高分辨率地震子波作为成像处理的核心目标,并据此开展薄层油气藏的识别、描述与评价。而描述任意介质中地震波传播的波动理论和贝叶斯参数估计理论构成了新一代高精度地震勘探的理论基础。“两宽一高”的地震数据采集技术和更高精度的子波处理;基于高维、字典基和稀疏特征表达的信号处理技术(解决去噪、数据规则化、数据压缩、去混叠等问题)、建立更精确速度和Q值模型以及估计宽带反射系数的特征波反演成像技术、宽带波阻抗成像技术和基于信息综合的人工智能油藏描述技术代表了走向精确地震勘探的未来方向。 展开更多
关键词 “两宽一高”地震数据 全波形反演 特征波反演 背景波阻抗建模 宽带方位角度反射系数 宽带波阻抗建模 精确油藏描述 机器学习与人工智能 精确地震勘探
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Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers 被引量:2
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作者 Chen Xu Huo Xiaofei +1 位作者 Wu Zhe Lu Jingjing 《Chinese Medical Sciences Journal》 CAS CSCD 2021年第3期196-203,共8页
Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply ar... Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed. 展开更多
关键词 artificial intelligence machine learning ovarian cancer radiomics ALGORITHM medical imaging
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Artificial intelligence and its application for cardiovascular diseases in Chinese medicine 被引量:2
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作者 CHEN Xiaotong LEUNG Yeuk-Lan Alice SHEN Jiangang 《Digital Chinese Medicine》 2022年第4期367-376,共10页
Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates o... Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs. 展开更多
关键词 Traditional Chinese medicine(TCM) Cardiovascular diseases(CVDs) Artificial intelligence(AI) Machine learning(ML) Deep learning(DL)
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Multi-agent reinforcement learning with cooperation based on eligibility traces
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作者 杨玉君 程君实 陈佳品 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2004年第5期564-568,共5页
The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavio... The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method. 展开更多
关键词 reinforcement learning MULTI-AGENT BEHAVIOR eligibility trace
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A Novel Hidden Danger Prediction Method in CloudBased Intelligent Industrial Production Management Using Timeliness Managing Extreme Learning Machine
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作者 Xiong Luo Xiaona Yang +3 位作者 Weiping Wang Xiaohui Chang Xinyan Wang Zhigang Zhao 《China Communications》 SCIE CSCD 2016年第7期74-82,共9页
To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A mac... To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods. 展开更多
关键词 prediction incremental learning extreme learning machine cloud service
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It might not sound sexy, but AI & AR are what's hot in retail
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作者 Flora 《China Textile》 2018年第2期54-55,共2页
When you go to Fashion Week,the talk is all about what’s trending in colors,cuts,hemlines,and finishes.When you go to retail seminars,it's about data.And how artificial intelligence(AI),machine learning,
关键词 but AI It might not sound sexy AR are what's hot in retail
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Assessment criteria for nonverbal interaction contents in r-learning
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作者 CHOI Jong-hong LEE Jong-yun YOON Heung-seob 《Journal of Central South University》 SCIE EI CAS 2013年第9期2388-2398,共11页
r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee ... r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee the advantages of r-learning contents with no limits of timc and place and with nonverbal interaction which are not in e-learning contents,in recent years,assessment criteria for r-learning contents are urgently rcquired.Therefore,the reliable and valid assessment criteria were developed for nonverbal interaction contents in r-learning,and its detailed research content is as follows.First,assessment criteria for nonverbal interaction in r-learning contents will be specified into gesture,facial expression,semi-verbal message,distance,physical contact and time.Second,the validity of the developed assessment criteria will be proved by statistics.Consequently,the assessment criteria for nonverbal interaction contents will be helpful when choosing the better r-learning content and producing the better r-learning content,and the reliability of school education is improved ultimately. 展开更多
关键词 r-learning r-learning contents assessment criteria nonverbal interaction contents confirmatory factor analysis construct validity
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Artificial intelligence in drug design 被引量:14
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作者 Feisheng Zhong Jing Xing +13 位作者 Xutong Li Xiaohong Liu Zunyun Fu Zhaoping Xiong Dong Lu Xiaolong Wu Jihui Zhao Xiaoqin Tan Fei Li Xiaomin Luo Zhaojun Li Kaixian Chen Mingyue Zheng Hualiang Jiang 《Science China(Life Sciences)》 SCIE CAS CSCD 2018年第10期1191-1204,共14页
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage... Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design. 展开更多
关键词 drug design artificial intelligence deep learning QSAR ADME/T
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One neural network approach for the surrogate turbulence model in transonic flows 被引量:2
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作者 Linyang Zhu Xuxiang Sun +1 位作者 Yilang Liu Weiwei Zhang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2022年第3期38-51,I0002,共15页
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul... With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective. 展开更多
关键词 Deep neural network Turbulence modeling TRANSONIC High Reynolds number
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