为了解古树保护研究现状与发展趋势,基于Web of Science(WoS)和中国知网(CNKI)数据库检索近30 a相关研究发文情况,并利用Bibliometrix工具和VOSviewer软件实现相关指标的可视化。结果表明,近年来古树保护研究在全球范围内受到广泛关注,...为了解古树保护研究现状与发展趋势,基于Web of Science(WoS)和中国知网(CNKI)数据库检索近30 a相关研究发文情况,并利用Bibliometrix工具和VOSviewer软件实现相关指标的可视化。结果表明,近年来古树保护研究在全球范围内受到广泛关注,美国和中国是该领域发文量最大的2个国家,中国逐渐成为该领域研究的主要力量;不同国家间存在广泛合作,但作者间合作不够密切;刊文最多的英文、中文期刊分别是《FOREST ECOLOGY AND MANAGEMENT》和《国土绿化》;研究热点向生物多样性、大型古树健康和美丽乡村等方向发展。展开更多
On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative o...On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative or qualitative data analysis.The DDE Sedimentary Data Group is responsible for the management of the sedimentary data on the DDE platform and has now developed into a group of nearly 40 disciplinary experts.展开更多
城市轨道交通车辆设备种类繁多、关系复杂,人工故障诊断普遍存在精度低,速度慢,错误率高等问题。以制动子系统为例,经对制动系统故障模式和故障产生逻辑关系研究后,采用故障树分析法(Fault Tree Analysis,FTA)对制动系统建立故障树模型...城市轨道交通车辆设备种类繁多、关系复杂,人工故障诊断普遍存在精度低,速度慢,错误率高等问题。以制动子系统为例,经对制动系统故障模式和故障产生逻辑关系研究后,采用故障树分析法(Fault Tree Analysis,FTA)对制动系统建立故障树模型,通过产生式规则建立制动系统故障诊断知识库,结合知识库与故障树模型进行故障诊断定位。实验表明,该方法能有效针对制动系统进行故障诊断,减少对车辆维护人员的依赖,提高车辆维护效率,实现城市轨道交通运维智能化。展开更多
传统民间棋艺——藏久棋,是一种承载着深厚藏族文明与灿烂文化的完备信息博弈游戏。鉴于藏久棋规则体系的复杂性与棋局变化的多样性,传统博弈搜索算法难以有效应对其复杂决策需求。为提升藏久棋博弈的智能水平,提出了一种融合先验知识...传统民间棋艺——藏久棋,是一种承载着深厚藏族文明与灿烂文化的完备信息博弈游戏。鉴于藏久棋规则体系的复杂性与棋局变化的多样性,传统博弈搜索算法难以有效应对其复杂决策需求。为提升藏久棋博弈的智能水平,提出了一种融合先验知识的蒙特卡洛树搜索(Monte Carlo tree search,MCTS)算法优化策略。在布局规划、行棋策略等关键阶段,基于深度强化学习,融合领域专家的先验知识设计了策略选择优化函数和评估函数。通过函数来有效指导MCTS的搜索过程,并训练出能够生成高质量着法的最佳模型。实验表明,改进的MCTS算法在对弈中取得显著效果。展开更多
The historical records of mechanical fault contain great amount of important information which is useful to identify the similar fault.The current fault diagnosis methods using historical records are inefficient to de...The historical records of mechanical fault contain great amount of important information which is useful to identify the similar fault.The current fault diagnosis methods using historical records are inefficient to deal with intuitive application and multicomponent multiphase fault diagnosis.Towards the problem,the rapid and intelligent fault diagnosis method based on system-phenomenon-fault （SPF） tree is proposed.The method begins with the physical system of the fault system,conceives the fault causes as leaves,the fault causes as leaves and the frequentness of fault as the interrelationship,and finally forms the fault tree with structural relationship of administrative subordination and flexible multi-granularity components.Firstly,the forming method of SPF tree is discussed;Secondly some basic definitions as synonymous branch,the tough degree of the branch,the dominant leaf,and the virtual branch are defined;and then,the performances including the merger of the dominant branches keeping dominant,the merger of the synonymous branches keeping dominant were proved.Furthermore,the merging,optimizing and calculating of virtual branch of SPF tree are proposed,the self-learning mechanism including the procedure and the related parameter calculation is presented,and the fault searching method and main fault statistics calculation are also presented based on SPF tree.Finally,the method is applied in the fault diagnosis of the certain type of embedded terminal to demonstrate fault information searching in the condition of the synonymous branch,the virtual branch merging and visual presentation of search results.The application shows that the proposed method is effective to narrow down the scope of searching fault and reduce the difficulty of computing.The proposed method is a new way to resolve the intelligent fault diagnosis problem of complex systems by organizing the disordering fault records and providing intuitive expression and intelligent computing capabilities.展开更多
The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited re...The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.展开更多
The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to th...The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.展开更多
Unlike the case in Mediterranean countries, where olive oil consumption is driven by habit or tradition, in a population where olive oil consumption rates are considerably low, it appears reasonable to suppose that th...Unlike the case in Mediterranean countries, where olive oil consumption is driven by habit or tradition, in a population where olive oil consumption rates are considerably low, it appears reasonable to suppose that the initial decision to buy a fairly expensive product—as is the case with olive oil in the Uruguayan market—may result from an individual’s overall interest in health-related issues and/or their acquaintance with relevant nutritional properties of the particular product—in this case, olive oil. Consumer subjective and objective knowledge, interest in health-related issues, and demographic variables were studied for their potential relationship (explanatory capacity) with olive oil consumption frequency, using a sample of 256 inhabitants of Montevideo (Uruguay). Several of the studied variables were found to relate to olive oil consumption, such as subjective and objective knowledge, age, education level, marital status, and interest in health-related issues. Subjective knowledge was found to have the highest explanatory capacity. An increase in subjective knowledge is therefore expected to lead to an increase in consumption frequency among regular olive oil consumers, while it may also encourage less frequent or non-consumers to purchase olive oil and become acquainted with the product.展开更多
文摘为了解古树保护研究现状与发展趋势,基于Web of Science(WoS)和中国知网(CNKI)数据库检索近30 a相关研究发文情况,并利用Bibliometrix工具和VOSviewer软件实现相关指标的可视化。结果表明,近年来古树保护研究在全球范围内受到广泛关注,美国和中国是该领域发文量最大的2个国家,中国逐渐成为该领域研究的主要力量;不同国家间存在广泛合作,但作者间合作不够密切;刊文最多的英文、中文期刊分别是《FOREST ECOLOGY AND MANAGEMENT》和《国土绿化》;研究热点向生物多样性、大型古树健康和美丽乡村等方向发展。
文摘On the platform of the Deep-time Digital Earth Program(DDE),sedimentary data are essential for achieving its scientific objectives.These data will take stratigraphic units as their core data carrier,for quantitative or qualitative data analysis.The DDE Sedimentary Data Group is responsible for the management of the sedimentary data on the DDE platform and has now developed into a group of nearly 40 disciplinary experts.
文摘城市轨道交通车辆设备种类繁多、关系复杂,人工故障诊断普遍存在精度低,速度慢,错误率高等问题。以制动子系统为例,经对制动系统故障模式和故障产生逻辑关系研究后,采用故障树分析法(Fault Tree Analysis,FTA)对制动系统建立故障树模型,通过产生式规则建立制动系统故障诊断知识库,结合知识库与故障树模型进行故障诊断定位。实验表明,该方法能有效针对制动系统进行故障诊断,减少对车辆维护人员的依赖,提高车辆维护效率,实现城市轨道交通运维智能化。
文摘传统民间棋艺——藏久棋,是一种承载着深厚藏族文明与灿烂文化的完备信息博弈游戏。鉴于藏久棋规则体系的复杂性与棋局变化的多样性,传统博弈搜索算法难以有效应对其复杂决策需求。为提升藏久棋博弈的智能水平,提出了一种融合先验知识的蒙特卡洛树搜索(Monte Carlo tree search,MCTS)算法优化策略。在布局规划、行棋策略等关键阶段,基于深度强化学习,融合领域专家的先验知识设计了策略选择优化函数和评估函数。通过函数来有效指导MCTS的搜索过程,并训练出能够生成高质量着法的最佳模型。实验表明,改进的MCTS算法在对弈中取得显著效果。
基金supported by National Hi-tech Research and Development Program of China (863 key Program,Grant No.2007AA040701)Chongqing Municipal Natural Science Foundation Project of China (Grant No. CSTC2010BB4295)+2 种基金Research Fund for the Doctoral Program of Higher Education of China (Grant No.20100191120004)Fundamental Research Funds for the Central Universities of China (Grant No. CDJXS11111136)Research Foundation of Chongqing University of Science and Technology,China(Grant No. CK2010Z10)
文摘The historical records of mechanical fault contain great amount of important information which is useful to identify the similar fault.The current fault diagnosis methods using historical records are inefficient to deal with intuitive application and multicomponent multiphase fault diagnosis.Towards the problem,the rapid and intelligent fault diagnosis method based on system-phenomenon-fault （SPF） tree is proposed.The method begins with the physical system of the fault system,conceives the fault causes as leaves,the fault causes as leaves and the frequentness of fault as the interrelationship,and finally forms the fault tree with structural relationship of administrative subordination and flexible multi-granularity components.Firstly,the forming method of SPF tree is discussed;Secondly some basic definitions as synonymous branch,the tough degree of the branch,the dominant leaf,and the virtual branch are defined;and then,the performances including the merger of the dominant branches keeping dominant,the merger of the synonymous branches keeping dominant were proved.Furthermore,the merging,optimizing and calculating of virtual branch of SPF tree are proposed,the self-learning mechanism including the procedure and the related parameter calculation is presented,and the fault searching method and main fault statistics calculation are also presented based on SPF tree.Finally,the method is applied in the fault diagnosis of the certain type of embedded terminal to demonstrate fault information searching in the condition of the synonymous branch,the virtual branch merging and visual presentation of search results.The application shows that the proposed method is effective to narrow down the scope of searching fault and reduce the difficulty of computing.The proposed method is a new way to resolve the intelligent fault diagnosis problem of complex systems by organizing the disordering fault records and providing intuitive expression and intelligent computing capabilities.
文摘The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.
文摘The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.
文摘Unlike the case in Mediterranean countries, where olive oil consumption is driven by habit or tradition, in a population where olive oil consumption rates are considerably low, it appears reasonable to suppose that the initial decision to buy a fairly expensive product—as is the case with olive oil in the Uruguayan market—may result from an individual’s overall interest in health-related issues and/or their acquaintance with relevant nutritional properties of the particular product—in this case, olive oil. Consumer subjective and objective knowledge, interest in health-related issues, and demographic variables were studied for their potential relationship (explanatory capacity) with olive oil consumption frequency, using a sample of 256 inhabitants of Montevideo (Uruguay). Several of the studied variables were found to relate to olive oil consumption, such as subjective and objective knowledge, age, education level, marital status, and interest in health-related issues. Subjective knowledge was found to have the highest explanatory capacity. An increase in subjective knowledge is therefore expected to lead to an increase in consumption frequency among regular olive oil consumers, while it may also encourage less frequent or non-consumers to purchase olive oil and become acquainted with the product.
